long-termrainfalltrendsandfutureprojectionsoverxijiang ......2019/10/09 · mann–kendall test...

Research ArticleLong-Term Rainfall Trends and Future Projections over XijiangRiver Basin China

Muhammad Touseef 12 Lihua Chen 12 Kaipeng Yang 12 and Yunyao Chen12

1College of Civil Engineering and Architecture Guangxi University Nanning 530004 China2Guangxi Key Laboratory of Disaster Prevention and Engineering Safety Nanning 530004 China

Correspondence should be addressed to Lihua Chen xdslclhgxueducn

Received 9 October 2019 Revised 31 January 2020 Accepted 11 February 2020 Published 12 March 2020

Academic Editor Francesco Viola

Copyright copy 2020 Muhammad Touseef et al +is is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work isproperly cited

Precipitation trend detection is vital for water resources development and decision support systems +is study predicts theclimate change impacts on long-term precipitation trends It deals with the analysis of observed historical (1960ndash2010) andarithmetic mean method in assembling precipitation from CMIP5 Global Climate Models (GCMs) datasets for a future period(2020ndash2099) under four emission scenarios Daily precipitation data of 32 weather stations in the Xijiang River Basin wereprovided by National Meteorological Information Centre (NMIC) of the China Meteorological Administration (CMA) andGlobal Climate Models (GCMs) with all four emission scenarios statistically downscaled using Bias Correction Special Dis-aggregation (BCSD) and applied for bias correction via Climate Change Toolkit (CCT) Nonparametric MannndashKendall test wasapplied for statistical significance trend analysis while the magnitude of the trends was determined by nonparametric Senrsquosestimator method on a monthly scale to detect monotonic trends in annual and seasonal precipitation time series +e resultsshowed a declined trend was observed for the past 50 years over the basin with negative values of MK test (Z) and Senrsquos slope QHistorical GCMs precipitation detected decreasing trends except for NoerESM1-Mwhich observed slightly increasing trends+eresults are further validated by historical precipitation recorded by the Climate Research Unit (CRU-TS-31) +e future scenarioswill likely be positive trends for annual rainfall Significant positive trends were observed in monsoon and winter seasons whilepremonsoon and postmonsoon seasons will likely be slightly downward trends+e 2040s will likely observe the lowest increase of66 while the 2050s will observe the highest increase of 115 over the 21st century under future scenarios However due to theuncertainties in CMIP5 the future precipitation projections should be interpreted with caution +us it could be concluded thatthe trend of change in precipitation around the Xijiang River Basin is on the increase under future scenarios +e results can bevaluable to water resources and agriculture management policies as well as the approach for managing floods and droughts underthe perspective of global climate change

1 Introduction

Quantifying rainfall on spatial and temporal scales has beenof great interest for experts during the past century becauseof the indication of the global positive trend even thoughnegative trends were was observed in large areas globally [1]Climate change and urbanization are two interlinked well-defined and increasing environmental phenomena in the21st century Economic development at the local scale as wellas global scale has affected the water resources Globalwarming is one of the major reasons for climate change

+ese two terms (global warming and climate change) alterthe average temperature of Earthrsquos climate system and re-lated effects China is in line with global warming but withspecific characteristics +e average annual temperatureincreased from the 1920s to the 1940s decreasing trendswere from the 1950s to 1980s and till date from the 1980sthe current temperature is rising +e latest decade in Chinawas recorded as the warmest period +ese trends wereobviously more in southern China than in western easternand northern China [2] Rapid changes in human activitiessignificantly imbalance the hydrological cycle which result

HindawiAdvances in MeteorologyVolume 2020 Article ID 6852148 18 pageshttpsdoiorg10115520206852148

in frequent occurrence of extreme events [3] Risks ofnatural floods due to numerous environmental changes andhuman activities created huge concerns for climate changeexperts [4] Frequent extreme precipitation events causesevere floods that lead to runoff [5]

Global climate changes altered precipitation patternsand global temperature increases which could have a sig-nificant impact on the local hydrological cycle [6] +isincrease in temperature and changes in the hydrologicalcycle raised stormwater flows which are easily understoodPrecipitation patterns over the urban areas are affected bychanges in surface albedo and vegetation cover All thesefactors increase runoff due to retardation of the infiltrationand evapotranspiration process [7] +e statistical down-scaling model (SDSM) and Statistical Analog ResamplingScheme (STARS) were used to downscale the GCM outputsfor projecting the future climate scenarios and performedwell in simulating temperature and precipitation [8]

China has very swift economic growth in the past fewdecades Urbanization which leads to significant impacts onland-use changes was an 85 million hectare square meter in2013 According to the National Bureau Statistics of Chinathe municipal population exceeds 50 in recent years andthis will be over 80 in 2050 by Yan et al [9] +esouthcentral and southwestern provinces of mainland Chinareceived the most prominent donors of migrants from 1995to 2000 [10 11] Pearl River Delta has not been a region ofrapid land conversion historically for hundreds of years butthe government directives in early 1980s regarding eco-nomic growth which directly upgraded the living standardsand urbanization rate of 300 in the delta have seriousimpacts on various climate observations [12 13] Since the1980s rapid economic growth and policy change turned thePearl River Delta (PRD) region as the fastest populatedregion [13] Water-Energy-Food Nexus alters as a result ofmigration from urban to rural areas owing to changes in theradiation process +e anthropogenic aerosols carbonemissions and high-rise buildings affect the air quality localweather and climate [14]+e anthropogenic forcing mainlyincludes the emissions of greenhouse gases (GHGs) as wellas land-useland-cover changes [15] Ren et al [16] pre-sented evidence for the rapid urbanization effect 005degC perdecade increase in temperature is recorded as a result ofurbanization in mainland China

China observed an increase of 11degC from 1908 to 2007 inaverage surface temperature [2 17] Extreme weather eventshave great impacts on the ecosystem and society Variousstudies were conducted throughout the world to analyze thenature of extreme events and concluded that future climatechange will increase the intensity and frequency of suchevents [18]

Recently changes in precipitation trends have attractedthe researcherrsquos attention Southern China observed a30ndash50 increase in precipitation in the winter season(December January and February) from 1900 to 1999[19 20] Standardized Precipitation Index (SPI) trendsacross the Pearl River Basin for the monsoon characterizedby decreasing SPI shows that dry days govern major parts ofthe Pearl River Basin while winter (December-February) is

characterized by increasing SPI trends [21] Variations of theannual and seasonal rainfall are not significant at gt95confidence level However substantial negative trends canbe observed in the number of wet days [22]

Liu et al [23] detected an increase of +18degC in annual airtemperature from 1961 to 2007 at Pearl River Basin Fischeret al [24] applied the MannndashKendall test to daily meantemperature for 157 stations and found significant positivetrends of annual mean temperature +e study also sum-marized that the whole basin observed positive trends inannual and monthly mean temperatures however thetemperature increased less in summer than in winter Zhanget al [25]applied the SWAT model to GCMsrsquo outputs inUrumqi River and both temperature and precipitation showincrease in near and far future

+e long-term average precipitation of the Pearl RiverBasin is nearly 1500mm Average of 2mm per decade isobserved in the changing rate in annual average precipita-tion by evaluating 42 rainfall stations 110 rainy days with 14days per decade is the changing magnitude for long-termannual average precipitation while 135mmday is the long-term annual average rainfall intensity with 014mmday perdecade changing magnitude [26]

Gemmer et al [27] also concluded their findings for 192stations (1961ndash2007) for annual monthly and daily sums inthe Pearl River Basin that autumn precipitation observeddeclined trends and spring summer and winter rainfall haveinclined trends +e same findings were supported by manyother researchers in their studies [22] +e East-Asianmonsoon plays a key role in local rainfall trends summa-rized by [28] that strong winter monsoon with northerlywinds is governed by declined trends in winter season oversouthern China

Gao et al [29] recommended that high-resolutionmodels are better to examine future climate projections overChina and East Asia Chen et al [30] evaluated historicalprecipitation variability over 21st century CMIP5 archiveestimates which are put into context based on the 20thcentury biases and concluded that CMIP5 models canproduce better spatial patterns over CMIP3 Feng et al [31]studied future projections based on the global AGCM overChina and concluded that annual precipitation is close to thestation data +e regional mean precipitation will increase innorthern regions greater than southern regions in Chinabased on the projections of 11 climate models under rep-resentative concentration pathway (RCP) scenarios [32]Similarly the Pearl River Basin will likely be inclined trendsin precipitation under RCP26 and RCP45 scenarioswhereas declined trends under RCP85 [33]

Guo et al [34] summarized that climate plays a key rolein changing basin hydrology streamflow in the Xijiang RiverBasin China +e Xijiang River Basin has the main tributaryof the Pearl River Basin which lies in the subtropical regionof South China +e Pearl River Basin is the third largestriver basin of China with more than 100 million peopleresiding Since 1990 the Xijiang River Basin observed fre-quent flood disasters due to heavy storm events [35] Aslightly increasing trend was observed historically(1951ndash2010) during the dry season of the Xijiang River Basin

2 Advances in Meteorology

[36] All these studies reveal that there are no significantsimilarities in rainfall trends at the regional level For themanagement and planning at the regional or local scale ithas been found that continental or global scale studies ofclimate variables are not very beneficial [37]+erefore localclimatic parameter studies are useful for better management+e rainfall trend analysis is important to evaluate theimpact of climate change therefore in this study an attempthas been made to determine the rainfall trends over theXijiang River Basin +e primary aim of the present study isto analyze the changes in annual and seasonal rainfall for thehistorical period of 1960ndash2010 and future rainfall trends forthe period of 2020ndash2099 using GCMs A number of re-searchers [27 35 38] have assessed the rainfall trends in thebasin and they found that seasonal variability is closelysimilar For this purpose MannndashKendall test [39] andKendall [40] are most widely used nonparametric tests[41ndash43] in this study to analyze annual and seasonal rainfalltrends in time series

2 Materials and Methods

21 Study Area +e selected study area is the Xijiang RiverBasin (Figure 1) which is the largest river basin contributingto the Pearl River Basin and located in South China+e totaldrainage area of the Xijiang River Basin is 305times105 km2

+e basin has a humid and tropical climate with plentifulprecipitation and generally high air temperature +e meanair temperature is nearly 14degCndash22degC +e mean annualprecipitation varies from 1200mm to 1900mm with adiverse increase from the west to east Precipitation mainlyoccurs from April to October which accounts for 72ndash86of the annual precipitation [38]

22DataAvailability Daily precipitation data of 32 weatherstations (Figure 2) in the Xijiang River Basin for the period of1960ndash2010 were provided by the National MeteorologicalInformation Centre (NMIC) of the China MeteorologicalAdministration (CMA)

221 Global Climate Models (GCMs) Data +is studyanalyzes the Climate Datasets from five (05) Global ClimateModels (GCMs) (GFDL-ESM2M HadGEM2-ES IPSL-CM5A-LR MIROC-ESM-CHEM and NorESM1-M) fromISI-MIP (Intersectoral Impact Model IntercomparisonProject) [44] with all four scenarios (RCP-26 RCP-45RCP-60 and RCP-85)+e raw GCMs output is statisticallydownscaled (delta method) and Bias Correction SpecialDisaggregation (BCSD) is applied for bias correction usingClimate Change Toolkit (CCT) [45ndash47] +is CCT packagealso includes historical climate data (1970ndash2006) from theClimate Research Unit (CRU-TS-31) which could be used asan observed dataset All Climate Datasets are 05 degreespatial resolution downscaled and are available in a simpletext format Climate Change Toolkit (CCT) extractsdownscales makes bias correction of and interpolates the

raw GCMs outputs +e package will analyze extreme eventsthat are dry and wet days and analyze the past floodingtrends in future data

23 Trend Analysis Long-term future and historical trendanalysis and estimation of Senrsquos slope are evaluated usingKendall and Sen [48 49] method respectively for givendatasets Parametric or nonparametric procedures are fol-lowed to detect a statistical trend which is a significantchange over time while trend analysis of a time seriesconsists of the magnitude of the trend and its statisticalsignificance [50] Nonparametric MannndashKendall test wasused for statistical significance trend analysis while themagnitude of the trends was determined by nonparametricSenrsquos estimator method

231 MannndashKendall Test MannndashKendall test is a non-parametric test for finding trends in time series +is test iswidely used because the data do not need to confirm anydistribution [51ndash53] +is test checks the null hypothesis ofno trend versus the alternative hypothesis of the presence ofmonotonic increasing or decreasing trend of hydroclimatictime series data +is test is more suitable for those timeseries where the trend may be considered as monotonic(consistently increasing or decreasing) Each data value inthe time series is compared with all subsequent values +eMannndashKendall test is applicable in cases when the datavalues xi of a time series can be assumed to obey themodel in

xi f ti( 1113857 + εi (1)

where f(t) is a monotonic function of time and the residualsεi can be supposed to be from the same distribution withzero means +e variance of the distribution is constant intime+is study considers the null hypothesis of no trendHothat is the observations xi are randomly ordered in timeagainst the alternative hypothesis H1 where there is anincreasing or decreasing monotonic trend +e net result ofall such increments and decrements gives the final value of S

S 1113944nminus1

i11113944

n

ji+1sgn xj minus xi1113872 1113873 (2)

where xj and xi are annual values n is the number of datapoints and sgn(xj minus xi) can be calculated using

sgn xj minus xi1113872 1113873

1 if xj minus xi gt 0

0 if xj minus xi 0

minus1 if xj minus xi lt 0

⎧⎪⎪⎪⎨

⎪⎪⎪⎩

(3)

A positive or negative value of S defines increasing ordecreasing trends respectively If the number of data n valueis 10 or more the S statistics behave as normally distributedand the test is performed with a normal distribution [54]+e mean variance and standard normal distribution (Zstatistics) is computed using

Advances in Meteorology 3

E(S) 0 (4)

Var (S) 118

n(n minus 1)(2n + 5) minus 1113944n

i1ti ti minus 1( 1113857 2ti + 5( 1113857⎡⎣ ⎤⎦

(5)

where n is the number of data points and ti is the number ofdata points in the ith group +e normal Z statistics arecomputed using

Z

S minus 1VAR(S)

1113968 if Sgt 0

0 if S 0

S + 1VAR(S)

1113968 if Slt 0

⎧⎪⎪⎪⎪⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎪⎪⎪⎪⎩

(6)

Negative Z value indicates a decreasing trend and thecomputed Z statistics is greater than the Z value

0 225 450 675 9001125kilometers

Xijiang basinvalueHigh 2844

Low 0

21deg0prime0PrimeN

22deg0prime0PrimeN

23deg0prime0PrimeN

24deg0prime0PrimeN

25deg0prime0PrimeN

26deg0prime0PrimeN

21deg0prime0PrimeN

22deg0prime0PrimeN

23deg0prime0PrimeN

24deg0prime0PrimeN

25deg0prime0PrimeN

26deg0prime0PrimeN

105deg0prime0PrimeE 110deg0prime0PrimeE

110deg0prime0PrimeE105deg0prime0PrimeE

0deg0prime0Prime

15deg0prime0PrimeN

30deg0prime0PrimeN

45deg0prime0PrimeN

90deg0prime0PrimeE 105deg0prime0PrimeE 120deg0prime0PrimeE 135deg0prime0PrimeE75deg0prime0PrimeE

0deg0prime0Prime

15deg0prime0PrimeN

30deg0prime0PrimeN

45deg0prime0PrimeN

75deg0prime0PrimeE 90deg0prime0PrimeE 105deg0prime0PrimeE 120deg0prime0PrimeE 135deg0prime0PrimeE

Figure 1 Location of the Xijiang River Basin


corresponding to the 5 level of significance A two-tailedtest is used for significance level α 01 005 001 and 0001005 significance level means that there is a 5 probabilitythat we make a mistake when rejecting null hypothesis H0

+e MannndashKendall test does not require that the data benormally distributed It is not affected by missing data otherthan the fact that the number of sample points is reducedand hence might affect the statistical significance adverselyMannndashKendall test output is not affected by the irregularspacing of the time points of measurement as well as thelength of the time series However the MannndashKendall test isnot suited for data with periodicities For this purpose allperiodic effects were removed by the prewhitening methodfrom the data in the processing step before computing theMannndashKendall test Secondly the MannndashKendall test tendsto give more negative results for shorter datasets the longerthe time series the more effective the trend detectioncomputation [41 42]

232 Senrsquos Slope Method Linear regression is one of themost widely used methods for detecting trends in timeseries However this method requires the assumption ofnormal distribution in residuals [55ndash57] Many studiesconcluded that hydrological variables give right skewnessdue to the influence of natural phenomena and do not followa normal distribution [58] Senrsquos slope method is non-parametric and used for predicting the magnitude (trueslope) and developing linear relationships [49] Senrsquos slope isestimated as the median of all pairwise slopes between eachpair of points in the dataset [59] Each individual slopemjk iscalculated using

mjk yj minus yk

j minus k (7)

where k 1 2 3 (nminus1) and j 2 3 n while yj and ykare data values at times j and k +e median of the n values ofmjk is represented by Senrsquos slope of estimation given by

111deg0prime0PrimeE

111deg0prime0PrimeE

108deg0prime0PrimeE

108deg0prime0PrimeE

105deg0prime0PrimeE

105deg0prime0PrimeE

102deg0prime0PrimeE

102deg0prime0PrimeE

27deg0prime0PrimeN 27deg0prime0PrimeN




0 190 380 570 76095kilometers

Xijiang basinvalue

High 2844

Low 0

Weather stations

Figure 2 Location of weather stations


Qmed

m(n+12) for n is odd

12

m(n2)1113872 +m(n+22)1113873 for n is even

⎧⎪⎪⎪⎨

⎪⎪⎪⎩

(8)

Positive Senrsquos estimator Qmed indicates an increasingtrend while negative Senrsquos slope indicates a falling trendQmed is computed using a 100(1minusα) confidence intervalusing a nonparametric test [54]

3 Results

31 Annual Rainfall Features +e initial analysis for thisstudy included computing the mean standard deviation(STD) coefficient of skewness (Cs) coefficient of kurtosis(Ck) and coefficient of variance (Cy) in the annual pre-cipitation for every station for 51 years (1960ndash2010) Rainfallcharacteristics of the Xijiang River Basin are presented inTable 1 +e mean annual precipitation varied between8513mm at a higher altitude at the upper basin and1883mm precipitation at the north of the basin in the Guilinarea For normal distribution coefficient of skewness andcoefficient of kurtosis values are 0 and 3 respectively Table 1indicates that for most of the station dataset is positivelyskewed and negative kurtosis represents light-tailed distri-bution Coefficient of variation represents the extent ofvariability of data sample relative to the mean of the pop-ulation +e coefficient of variation varied between 131 atDushan station and 222 at Guangnan station +e averagespatial variability of the precipitation over the Xijiang RiverBasin is 17

32 Historical Temporal Precipitation Trends on Seasonal andAnnual Scale Long-term historical trends were assessed inthis study for the period of 1960ndash2010 +e MannndashKendall(MK) test was applied on a monthly scale to detect trends inprecipitation time series Figure 3 presents the mean annualmonsoon JJA (JunendashAugust) Winter DJF (Decem-berndashFebruary) premonsoon MAM (MarchndashMay) andpostmonsoon SON (SeptemberndashNovember) precipitation+emean annual precipitation is 1360mm for the basin+edeclined trend is observed for the past 50 years over the basinwithMK test Z value minus071 and Senrsquos slopeQ value of minus1063Average rainfall in the monsoon season was 670mm whichwas 493 contribution to the annual rainfall A slightlyincreasing trend was recorded in average monsoon pre-cipitation with MK test Z value of 034 and Senrsquos slope Qvalue is 0247 Winter season is almost dry having an averagerainfall of 9527mm precipitation over the basin Winterseason contributed with 7 rainfall to the annual meanprecipitation with the significant increasing trend of MK testZ value 192 and Senrsquos slopeQ value 0 631 Premonsoon andpostmonsoon observed decreasing trends with a meanprecipitation of 35863mm and 23537mm respectively

Premonsoon also got significant rainfall which con-tributed with 264 while postmonsoon contributed onlywith 1732 to the annual mean rainfall over the basin MKtest Z statistics for premonsoon and postmonsoon are minus076

and minus226 respectively Senrsquos slope Q value is minus0430 andminus1344 respectively Postmonsoon (SeptemberndashNovember)observed a significant decrease while the Winter season(DecemberndashFebruary) observed substantial inclination(Figures 4(a)ndash4(e))

33 Spatial Distribution of Historical Rainfall TrendsElevation affects precipitation significantly especially inhilly areas Spatial variation in rainfall trends over theXijiang Basin was significant in the past few decades Lowaltitude areas received a significant amount of rainfallUpper Xijiang Basin consisting of Nanpanjiang and Bei-panjiang is at higher altitudes (gt1500meters) which receivedless precipitation relative to lower altitudes Guilin GaoyaoDuanWangmo and other similar areas+e arid conditionsof the higher altitudes in the basin are because of the leewardside of themountain Table 2 presents theMK test Z statisticsand Senrsquos slope S statistics of stations

+e above table concluded that the average values of Zand Q statistics for annual rainfall are minus0394 and minus0776respectively +ese values summarized that there was adeclining trend over the Xijiang Basin +e trends werevarying but 21 stations observed a decrease in precipitationLongzhou station which is at low altitude has the lowestSenrsquos slope Q magnitude while Mengshan station has thehighest Senrsquos slope Qmagnitude value Monsoon observed aslight increase with an average Senrsquos slope Q magnitude of0177 over the basin 16 stations have declined trend whilethe remaining showed positive trends Guilin station has asignificant increasing trend in monsoon season with SenrsquosslopeQmagnitude value of 4550 while Nanning has a slightdecline trend with the lowest Q magnitude of minus0032 inmonsoon season Winter season observed increasing trendwith Z statistics 133 and Senrsquos slopeQmagnitude of 078 Allstations observed increasing trends in the winter seasonPremonsoon and postmonsoon seasons were influenced bydeclining trends All stations observed decreasing trendsover the Xijiang River Basin in postmonsoon while 18stations showed negative trends in premonsoon

Guilin station situated at the lower basin has an averagemean precipitation of 188333mm Annual rainfall has aslightly increasing trend in Figure 5(a) winter and monsoonseasons have a significant increase in Figures 5(b) and 5(c)while premonsoon and postmonsoon Figures 5(d) and 5(e)observed a decreasing trend in precipitation

Figures 6(a)ndash6(e) represent the annual and seasonalmean precipitation trends of Zhanyi station which is situatedin the upper basin +is station received less amount ofprecipitation in history Annual precipitation was signifi-cantly decreased Similar declination was followed bymonsoon and postmonsoon mean precipitation +is areaobserved increasing trends in winter and premonsoonseason

34 Future Precipitation Trends +is study projected thefuture prediction of precipitation Climate Datasets using thearithmetic mean (AM) assemble of five (05) Global ClimateModels (GCMs) (GFDL-ESM2M HadGEM2-ES IPSL-


Table 1 Summary of geographic conditions and mean annual precipitation statistics for the study area

Station name Station number Longitude Latitude Elevation (m) Mean (mm) STD Cs Ck Cv

Wei Ning 56691 10428 2687 22375 8791 1612 05 minus06 183Zhanyi 56786 10383 2558 18987 8671 1671 06 minus07 17Panxian 56793 10462 2578 15152 12473 2197 04 12 16Yuxi 56875 10255 2435 16367 9029 1517 03 14 168Luxi 56886 10377 2453 17043 9178 1531 05 03 167Mengzi 56985 10338 2338 13007 8513 1513 minus01 minus04 178Anshun 57806 10592 2625 13929 13295 2210 minus03 02 166Xingyi 57902 10518 2543 13785 12242 2186 01 05 164Wangmo 57906 10608 2518 5668 12387 1848 00 03 149Luodian 57916 10677 2543 4403 11414 2020 01 minus07 177Dushan 57922 10755 2583 10133 13117 1715 00 minus02 131Rongjiang 57932 10853 2597 2857 14362 1986 01 minus07 167Rongan 57947 10940 2522 1213 17859 2798 02 minus03 148Guilin 57957 11030 2532 1644 18833 3265 01 07 173Guangnan 59007 10507 2407 12496 10537 2339 03 117 222Fengshan 59021 10703 2455 4846 15304 2788 02 minus04 182Hechi 59023 10805 2470 211 18724 2885 06 51 154Duan 59037 10810 2393 1708 17251 2892 minus01 minus04 168Liuzhou 59046 10940 2435 968 14451 3078 01 00 213Mengshan 59058 11052 2420 1457 17438 3194 06 minus02 183Hezhou 59065 11152 2442 1088 15526 3067 06 01 198Napo 59209 10583 2342 7936 13856 2073 minus01 minus05 150Baise 59211 10660 239 17350 1322 2229 minus01 minus06 203Jingxi 59218 10642 2313 7394 16293 2603 minus01 minus02 160Laibin 59242 10923 2375 8490 13418 2525 05 minus05 188Guiping 59254 11008 2340 4250 17123 3255 01 01 190Wuzhou 59265 1113 2348 1148 14683 2394 00 minus02 163Gaoyao 59278 11247 2305 71 16475 2665 00 minus06 162Longzhou 59417 10685 2233 1288 12822 2241 00 minus07 175Nanning 59431 10835 2282 731 14131 2360 04 05 181Xinyi 59456 11093 2235 846 17775 3788 00 minus02 213Louding 59462 11157 2277 533 15406 2525 05 minus05 188

Annual average precipitationMonsoonWinter

PremonsoonPostmonsoon

0

200

400

600

800

1000

1200

1400

1600

1800

Prec

ipita

tion

(mm

)

1970 1980 1990 2000 20101960Time (year)

Figure 3 Annual and seasonal average precipitation trends over the Xijiang Basin (1960ndash2010)


CM5A-LR MIROC-ESM-CHEM and NorESM1-M) fromISI-MIP (Intersectoral Impact Model IntercomparisonProject) Future Global Climate Datasets are available for(2006ndash2099) and historical GCMs (1950ndash2005) shown inFigure 7 as a baseline +is study analyzed future dailyprecipitation GCMs data over the Xijiang River Basin for the

period of 2020ndash2099 Raw GCMs data were statisticallydownscaled using Bias Correction Special Disaggregation(BCSD) applied to remove Bias GCMs future precipitationstatistics are summarized in Table 3

+e historical precipitation over the Xijiang River Basinshowed similar characteristics with that of observed

100000

90000

80000

70000

60000

50000

Mon

soon

40000

30000

20000

10000

0001950 1960 1970 1980 1990 2000 2010 2020

Year

DataSenrsquos estimate

(a)

25000

20000

15000

Win

ter

10000

5000

0001950 1960 1970 1980 1990 2000 2010 2020

Year


(b)60000

50000

Prem

onso

on

40000

30000

20000

10000

0001950 1960 1970 1980 1990 2000 2010 2020

Year


(c)

Postm

onso

on

45000

30000

35000

40000

20000

25000

10000

15000

5000

0001950 1960 1970 1980 1990 2000 2010 2020

Year


(d)180000

160000

140000

120000

100000

80000

60000

40000

20000

000

Ann

ual

1950 1960 1970 1980Year

1990 2000 2010 2020


(e)

Figure 4 (andashe) Senrsquos slope estimator for annual and seasonal precipitation


historical precipitation with an annual mean precipitation of1500mm Skewness is positive while the dataset is light-tailed distribution

341 Future Projections in Annual and Seasonal Rainfall+ere are considerable uncertainties associated with pro-jecting changes for future rainfall projections +ese un-certainties may rise from different GCM outputs andscenarios +e three assumptions in these GCMs outputs areas follows predictors are variables of importance and arerealistically modeled by the host GCM the empirical rela-tionship is valid under changing climatic conditions and thepredictors employed fully represent the climate changesignal [60] In this study five GCMs outputs for all scenarioswere analyzed and the bias was removed based on biascorrection spatial disaggregation (BCSD) method

Historical GCMs output in Table 4 has similar trendsrecorded by observed rainfall in Table 5 Four out of fiveGCMs in Figure 8 have decreasing trends in average annualand seasonal historical precipitation while NoerESM1-Mhasa slightly increasing trend Climate Research Unit (CRU-TS-31) historical data Table 4 which was baseline data for biascorrection also have decreasing trends Annual mean

precipitation output for future scenarios has likely to beinclined trends except for IPSL-CM5A-LR which showedthe contrast in trends and all scenarios have negative MK Zstatistics and negative Senrsquos slope Q magnitude marked inTable 6 Seasonal precipitation will likely have increasedtrends in rainfall in future scenarios Few scenarios havenegative trends that prove the existence of uncertainties inGCMs output

35 Decadewise Annual and Seasonal RainfallDecadewise annual and seasonal observed rainfall and meanof the future projections under all four scenarios depicted inFigures 9 and 10 respectively In the 2010s the basin re-ceived the lowest annual rainfall of 131350mm while thepredecade 2000s received the highest rainfall of 14072mm+e arithmetic mean of annual precipitation for the past 51years (1960ndash2010) was 1360mm Considering this value as abaseline RCP-26 predicts change of 92 RCP-45 predictschange of 804 and RCP-60 will likely observe the highestchange of 979 and RCP-85 with the lowest change of71 as reported in Table 7

Decadal future projections for five GCMs under all fouremission scenarios presented in Figure 10 predict that 2050s

Table 2 MK Test Z and Senrsquos slope estimator Q of annual and seasonal observed rainfall at Xijiang Basin

Station name Station numberAnnual Monsoon Winter Premonsoon Postmonsoon

Z Q Z Q Z Q Z Q Z QWei Ning 56691 minus167 minus284 minus067 minus0640 304 0324 minus062 minus0358 minus185 minus1276Zhanyi 56786 minus172 minus2989 minus202 minus2917 354 0904 096 0530 minus192 minus1625Panxian 56793 minus109 minus2473 minus070 minus1080 258 0644 002 0029 minus269 minus2650Yuxi 56875 000 0018 minus008 minus0072 180 0452 159 0885 minus115 minus0783Luxi 56886 minus195 minus3014 minus220 minus1779 099 0225 075 0456 minus193 minus1432Mengzi 56985 minus010 minus0150 minus076 minus0820 080 0236 154 0941 minus080 minus0427Anshun 57806 minus135 minus2600 016 0306 130 0311 minus161 minus1729 minus158 minus1429Xingyi 57902 minus029 minus0741 111 1421 126 0293 minus062 minus0659 minus154 minus1578Wangmo 57906 073 1863 058 1121 004 0011 minus070 minus0576 minus019 minus0140Luodian 57916 067 1345 057 0994 084 0219 089 0869 minus092 minus0715Dushan 57922 016 0314 057 0900 172 0739 017 0200 minus188 minus1835Rongjiang 57932 067 1596 079 1345 249 1027 087 0750 minus079 minus0648Rongan 57947 minus006 minus0075 130 3461 176 1281 minus073 minus0900 minus240 minus2340Guilin 57957 054 2029 184 4550 169 1529 minus060 minus1095 minus197 minus1850Guangnan 59007 minus136 minus2600 minus084 minus1443 081 0186 minus088 minus0680 minus281 minus1748Fengshan 59021 minus013 minus0408 019 0484 215 8000 minus037 minus0411 minus125 minus1089Hechi 59023 minus083 minus2338 minus097 minus1500 164 0796 minus076 minus1229 minus208 minus1623Duan 59037 minus052 minus1419 minus023 minus0521 096 0540 006 0089 minus161 minus1537Liuzhou 59046 006 0396 032 0896 136 0822 minus018 minus0237 minus180 minus1503Mengshan 59058 076 2403 148 3708 135 1127 minus082 minus0980 minus242 minus2092Hezhou 59065 030 0897 047 0709 074 0678 minus019 minus0253 minus145 minus1309Napo 59209 minus153 minus2926 minus096 minus0920 058 0182 minus026 minus0272 minus145 minus1488Baise 59211 minus037 minus0783 minus041 minus0635 123 0293 019 0233 minus086 minus0817Jingxi 59218 065 1611 029 0640 156 0641 019 0173 minus031 minus0250Laibin 59242 minus101 minus2827 minus075 minus1408 085 0490 minus032 minus0333 minus085 minus0535Guiping 59254 075 1768 063 1425 108 0897 minus090 minus1576 minus101 minus1208Wuzhou 59265 minus084 minus1897 024 0553 102 0692 minus149 minus1932 minus159 minus1572Gaoyao 59278 minus042 minus1609 minus102 minus1600 106 0570 054 0942 minus083 minus1113Longzhou 59417 minus130 minus3087 minus068 minus1236 091 0295 minus065 minus0586 minus188 minus1513Nanning 59431 minus101 minus2226 000 minus0032 041 0158 minus159 minus1422 minus140 minus1355Xinyi 59456 minus034 minus1733 010 0193 047 0116 minus023 minus0376 minus128 minus1380Louding 59462 000 minus0023 minus026 minus0432 052 0380 041 0689 minus090 minus1253



000

50000

100000

150000

200000

250000

300000A

nnua

l

1960 1970 1980 1990 2000 2010 20201950Year

(a)


1960 1970 1980 1990 2000 2010 20201950Year

000

20000

40000

60000

80000

100000

120000

140000

160000

Mon

soon

(b)


000

5000

10000

15000

20000

25000

30000

35000

40000

Win

ter

1960 1970 1980 1990 2000 2010 20201950Year

(c)


000

20000

40000

60000

80000

100000

120000

140000

160000Pr

emon

soon

1960 1970 1980 1990 2000 2010 20201950Year

(d)


000

10000

20000

30000

40000

50000

60000

Postm

onso

on

1960 1970 1980 1990 2000 2010 20201950Year

(e)

Figure 5 (andashe) Senrsquos slope estimator of Guilin station (highest average annual rainfall 188333mm)



000

20000

40000

60000

80000

100000

120000

140000A

nnua

l

1960 1970 1980 1990 2000 2010 20201950Year

(a)


000

10000

20000

30000

40000

50000

60000

70000

80000

90000

Mon

soon

1960 1970 1980 1990 2000 2010 20201950Year

(b)


000

2000

4000

6000

8000

10000

12000

Win

ter

1960 1970 1980 1990 2000 2010 20201950Year

(c)


000

5000

10000

15000

20000

25000

30000

35000

40000Pr

emon

soon

1960 1970 1980 1990 2000 2010 20201950Year

(d)


000

10000

20000

30000

40000

50000

60000

Postm

onso

on

1960 1970 1980 1990 2000 2010 20201950Year

(e)

Figure 6 (andashe) Senrsquos slope estimator of Zhanyi station (lowest average annual rainfall 8671mm)


will likely receive the highest amount of rainfall 154126mmwhile the 2040s will likely observe the lowest value of144922mm +e middle of the 21st century will likely ob-serve the lowest and highest rainfall in consecutive decades

4 Discussion

+e present study is an attempt to evaluate the spatial andtemporal variability trends in observed rainfall (1960ndash2010)

GFDL-ESM2MHad-GEM2-ESIPSL-CM5A-LR

MIROCNoerESM1-MObserved historical

500

700

900

1100

1300

1500

1700

1900

2100

Aver

age a

nnua

l pre

cipi

tatio

n (m

m)

1960 1970 1980 1990 20001950Year

Figure 7 Annual average precipitation of historical GCMs and observed historical precipitation

Table 3 Annual precipitation statistics of Global Climate Models (GCMs) over the study area

ISI-MIP model Scenarios Period Mean (mm) STD Cs Ck Cv

GFDL-ESM2M

Historical 1950ndash2005 15102 2399 02 minus01 159RCP-26 2020ndash2099 14983 22541 038 00 150RCP-45 2020ndash2099 14806 24119 minus008 00 163RCP-60 2020ndash2099 15057 20880 minus025 086 139RCP-85 2020ndash2099 14630 23042 019 minus06 157

Had-GEM2-ES

Historical 1950ndash2005 15073 1364 03 18 910RCP-26 2020ndash2099 15799 17461 010 minus04 111RCP-45 2020ndash2099 15383 16888 045 minus04 110RCP-60 2020ndash2099 15472 18785 minus009 085 121RCP-85 2020ndash2099 16151 18956 minus004 minus052 117

IPSL-CM5A-LR

Historical 1950ndash2005 15024 2153 minus02 minus060 143RCP-26 2020ndash2099 14082 20494 006 1077 146RCP-45 2020ndash2099 15112 22305 039 minus02 1476RCP-60 2020ndash2099 14477 21998 019 078 1519RCP-85 2020ndash2099 13295 19257 033 01 145

MIROC

Historical 1950ndash2005 15633 2523 02 minus02 161RCP-26 2020ndash2099 14912 22755 006 20 153RCP-45 2020ndash2099 15013 24247 007 minus01 162RCP-60 2020ndash2099 13820 21840 026 minus0642 158RCP-85 2020ndash2099 15103 22360 006 minus005 148

NoerESM1-M

Historical 1950ndash2005 15054 1746 01 05 116RCP-26 2020ndash2099 16165 19833 minus03 minus07 123RCP-45 2020ndash2099 16339 21510 05 minus02 132RCP-60 2020ndash2099 14914 1840 05 01 123RCP-85 2020ndash2099 15593 18470 050 02 1180

CRU Observed (historical) 1970ndash2006 15019 1330 070 890


Table 4 MK test Z and Senrsquos slope estimator Q of annual and seasonal future rainfall at the Xijiang Basin

ISI-MIP model Scenarios PeriodAnnual Monsoon Winter Premonsoon PostmonsoonZ Q Z Q Z Q Z Q Z Q

GFDL-ESM2M

Historical 1950ndash2005 minus152 minus317 minus151 minus126 minus076 minus023 minus076 minus086 minus131 minus135RCP-26 2020ndash2099 minus037 minus037 019 0117 005 0018 minus018 minus009 minus067 minus034RCP-45 2020ndash2099 064 073 minus002 minus002 018 005 minus112 minus055 157 089RCP-60 2020ndash2099 135 122 216 121 140 040 011 006 minus005 minus003RCP-85 2020ndash2099 minus102 minus137 128 084 minus216 minus056 minus246 minus114 033 023

Had-GEM2-ES

Historical 1950ndash2005 minus151 minus159 minus115 minus071 minus230 minus044 minus124 minus104 121 063RCP-26 2020ndash2099 167 155 129 068 minus012 minus002 104 039 075 037RCP-45 2020ndash2099 229 202 164 087 300 038 068 029 118 053RCP-60 2020ndash2099 308 245 310 165 108 016 minus089 minus031 241 111RCP-85 2020ndash2099 372 326 170 082 329 049 minus084 minus033 371 204

IPSL-CM5A-LR

Historical 1950ndash2005 minus100 minus198 029 020 minus095 minus061 minus009 minus011 minus108 minus095RCP-26 2020ndash2099 minus084 minus089 minus037 minus021 017 005 minus096 minus033 minus032 minus021RCP-45 2020ndash2099 minus032 minus046 115 054 minus195 minus037 minus194 minus094 074 042RCP-60 2020ndash2099 minus076 minus090 minus030 minus014 minus123 minus037 minus120 minus058 065 037RCP-85 2020ndash2099 minus054 minus044 152 078 minus310 minus056 minus197 minus078 047 021

MIROC

Historical 1950ndash2005 minus204 minus485 minus214 minus282 minus118 minus064 minus093 minus074 009 008RCP-26 2020ndash2099 172 184 095 069 152 046 016 012 060 031RCP-45 2020ndash2099 251 324 061 055 098 034 175 095 120 053RCP-60 2020ndash2099 090 106 002 003 181 041 064 031 051 021RCP-85 2020ndash2099 232 275 227 196 075 026 187 094 minus183 minus083

NoerESM1-M

Historical 1950ndash2005 060 094 129 109 minus101 minus030 minus029 minus021 minus066 minus044RCP-26 2020ndash2099 minus009 minus007 minus028 minus025 minus010 minus003 137 058 minus081 minus049RCP-45 2020ndash2099 369 428 327 2531 minus055 minus015 235 101 063 028RCP-60 2020ndash2099 219 204 184 138 015 002 161 051 115 052RCP-85 2020ndash2099 388 336 080 045 187 048 307 112 256 123

CRU Observed (historical) 1970ndash2006 minus020 minus035 111 208 069 027 minus056 minus061 minus145 minus145

Table 5 MannndashKendall test Z trend statistics of historical annual and seasonal average precipitation

Station name Station number Annual Monsoon Winter Premonsoon Postmonsoon

Wei Ning 56691Zhanyi 56786Panxian 56793Yuxi 56875Luxi 56886Mengzi 56985Anshun 57806Xingyi 57902Wangmo 57906Luodian 57916Dushan 57922Rongjiang 57932Rongan 57947Guilin 57957Guangnan 59007Fengshan 59021Hechi 59023Duan 59037Liuzhou 59046Mengshan 59058Hezhou 59065Napo 59209Baise 59211Jingxi 59218Laibin 59242Guiping 59254Wuzhou 59265Gaoyao 59278Longzhou 59417Nanning 59431Xinyi 59456Louding 59462


and future rainfall analyzing Global Climate Models(GCMs) datasets for historical period (1950ndash2006) and fu-ture (2020ndash2099) over the Xijiang River Basin +e analysisand detection of trends in the annual monsoon winterpremonsoon and postmonsoon seasons have been carriedout in 32 weather stations of the Xijiang River Basin +epast century rainfall behavior observed the low variations intheir anomaly Based on the MannndashKendall test and Senrsquosslope estimator annual premonsoon and postmonsoonaverage rainfall have decreasing trends while all-weatherstations observed increasing trends in the winter seasonMonsoon season observed a slight increase with an averageQ value of 0177 Similar results were achieved by Zhang et al[22]+ey found increased number of rainy days in the PearlRiver Basin +e coefficient of variation for average annualrainfall at Dushan station is the lowest at 131 while atGuangnan station CV is 222 +e main findings of thisstudy were the low precipitation and declining trends at

higher altitudes in the upper Xijiang Basin while the lowerXijiang Basin at lower altitudes recorded high precipitationand trends were inclined+is is consistent with the study byWang et al [61] Historical precipitation for four out of fiveGCMs observing decreasing trends having average Senrsquosslope magnitude Q minus109 NoerESM1-M observed a slightincrease in annual average historical precipitation with a Qvalue of 077 +e results are further validated by historicalprecipitation recorded by the Climate Research Unit (CRU-TS-31) CRU annual premonsoon and postmonsoon meanhistorical precipitation have decreasing trends while mon-soon and winter seasons have increasing trends +e resultsare further supported by several studies [35 38] +e lowestscenario RCP-26 showed variation in the trends Had-GEM2ES and MIROC will likely to observe increasingtrends while the rest of the three GCMs have negative trendsin annual precipitation RCP-45 recorded positive trendsexcept for IPSL-CM5A-LR for annual precipitation RCP-60

0

500

1000

1500

2000

250019

40

1950

1960

1970

1980

1990

2000

2010

Ann

ual

Year

0

500

1000

1500

2000

2500

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

1940

1950

1960

1970

1980

1990

2000

2010

Ann

ual

Year

01020304050607080

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0102030405060708090

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

20

40

60

80

100

120

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

20

40

60

80

100

120

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

1940

1950

1960

1970

1980

1990

2000

2010

Ann

ual

Year

0

500

1000

1500

2000

2500

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0200400600800

100012001400160018002000

Ann

ual

Year

0

500

1000

1500

2000

2500

1940

1950

1960

1970

1980

1990

2000

2010

Ann

ual

MIR

OC

Noe

rESM

1-M

Year

020406080

100120140160180200

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0200400600800

100012001400160018002000

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

Ann

ual

Year

0200400600800

100012001400160018002000

1940

1950

1960

1970

1980

1990

2000

2010

Ann

ual

Year

0

500

1000

1500

2000

2500

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

Ann

ual

Year

Historical

GFD

L-ES

M2M

Had

-GEM

2ES

IPSL

-CM

5A-L

R

RCP-26 RCP-45 RCP-60 RCP-85


























Figure 8 Historical RCP-26 RCP-45 RCP-60 and RCP-85 MK Z statistics and Senrsquos slope estimator


follows the negative trends positive trends observed in Had-GEM2ES and MIROC +e highest scenario RCP-85 haslikely to be increasing trends in annual precipitation exceptfor IPSL-CM5A-LR which is likely to be negative trends It isconcluded that four GCMs have likely to be increasingtrends in annual mean precipitation while IPSL-CM5A-LRhas likely to be negative trends in all four scenarios Futureprecipitation for monsoon and winter seasons is likely tohave increasing trends for higher scenarios +e lowestscenario RCP-26 of IPSL-CM5A-LR and NoerESM1-M hasnegative trends for monsoon and winter mean precipitationLi et al [62] reported similar results and used six GCMs

under three emission scenarios to project the potentialspatiotemporal changes over Loess Plateau of China duringthe 21st century and the projected changes were significantPremonsoon and postmonsoon precipitation will likelyfollow the same positive trends for future precipitationexcept for a few scenarios which account for less than 20+e arithmetic mean of annual precipitation for the past 51years (1960ndash2010) was 1360mm Considering this value as abaseline RCP-26 predicts a change of 92 RCP-45 pre-dicts a change of 804 and RCP-60 will likely observe thehighest change of 979 and RCP-85 with the lowest changeof 71 Similar results reported that long-term precipitation

Table 6 MannndashKendall test Z trend statistics of future (GCMs) annual and seasonal average precipitation

ISI-MIP model Scenarios Period Annual Monsoon Winter Prmonsoon Postmonsoon

GFDL-ESM2M

Historical 1950ndash2005RCP-26 2020ndash2099RCP-45 2020ndash2099RCP-60 2020ndash2099RCP-85 2020ndash2099

Had-GEM2-ES


IPSL-CM5A-LR


MIROC


NoerESM1-M


CRU Observed (historical) 1970ndash2006

AnnualMonsoonWinter


1980s 1990s 2000s 2010s1970sDecadal years

0200400600800

1000120014001600

Prec

ipita

tion

(mm

)

Figure 9 Decadal segmentation of the annual and seasonal observed rainfall


is projected to increase 60 under rcp26 and 120 underthe rcp85 scenario over Tibetan Plateau [32 63] +is studyconcludes that 80 of emission scenarios will likely observepositive trends for annual and seasonal future precipitationUncertainties exist in GCMs data and future projections ofhydrological parameters which is less than 20 observed bythis study

5 Conclusion

In this study we evaluate the long-term observed precipi-tation trend and five GCMs dataset used in the CMIP5 overthe Xijiang River Basin at 32 weather stations +ere wasconsistency in the results acquired from the MannndashKendallSenrsquos slope estimator test and the trend line for all stationsduring the specified study period +e trend line shows theincreasing and decreasing rainfall for stations +e trend inprecipitation observed for each station could imply that thechanges are more pronounced for certain locations and lessfor others Annual precipitation for the past half centuryobserved a decreasing trend Similarly winter and monsoonhave increasing trends while premonsoon and postmonsoonhave downwards trends+e historical precipitation over theXijiang River Basin showed similar characteristics comparedwith those of observed historical precipitation with an an-nual mean precipitation of 1500mm Skewness is positivewhile the dataset is light-tailed distribution Annual meanprecipitation output for future scenarios has likely to beinclined trends except for IPSL-CM5A-LR which showed a

negative trend Decadal segmentation of arithmetic mean offuture scenarios concluded that projected precipitation willincrease by 86 +e reason for these variations needsfurther study to link the observed trends with climatevariability +us the change in trends of rainfall becomes ashred of evidence across the study region to reach a con-clusion +ese results will possibly enhance the risk for bothagriculture and flooding in both urban and rural areas+erefore appropriate flood-control actions should be takento enhance human mitigation to flood hazards under thechanging climate across the Xijiang River Basin

Data Availability

Daily precipitation data of 32 weather stations in the XijiangRiver Basin for the period of 1960ndash2010 were provided byNational Meteorological Information Centre (NMIC) of theChina Meteorological Administration (CMA)

Conflicts of Interest

+e authors declare that they have no conflicts of interest

Acknowledgments

+is study was supported by the National Key Research andDevelopment Program of China (2017YFC0405900) Na-tional Natural Science Foundation of China (51669003) andGuangxi Key RampD Program (AB16380284)

RCP26RCP45

RCP60RCP85

1200

1250

1300

1350

1400

1450

1500

1550

1600

1650

1700

Prec

ipita

tion

(mm

)

2040s 2050s 2060s 2070s 2080s 2090s 2100s2030sDecadal years

Figure 10 Mean decadal segmentation of the annual rainfall from five GCMs under four emission scenarios

Table 7 Decadal mean rainfall (percent change) derived from observed (1960ndash2010) mean rainfall value 1360mm and arithmetic mean offive GCMs under four emission scenarios

Scenario 2030s 2040s 2050s 2060s 2070s 2080s 2090s 2100sRCP-26 1467 (79) 1457 (717) 1615 (158) 1591 (145) 1456 (66) 1477 (79) 1418 (409) 1507 (97)RCP-45 14689 (80) 13714 (084) 1633 (167) 1449 (615) 1447 (605) 1444 (58) 1516 (1029) 1518 (104)RCP-60 15239 (1205) 14375 (57) 15007 (93) 1432 (503) 1518 (104) 1515 (102) 1584 (141) 1532 (112)RCP-85 146845 (797) 1532 (127) 14155 (392) 15101 (99) 1461 (69) 1396 (25) 1507 (978) 1405 (324)


References

[1] R Pachauri L Meyer G Plattner and T Stocker IPCC 2014Climate Change 2014 Synthesis Report Geneva Switzerland2014

[2] S Wang and Z J C R Zhang ldquoEffects of climate change onwater resources in Chinardquo Climate Research vol 47 no 1-2pp 77ndash82 2011

[3] X Song S Song W Sun et al ldquoRecent changes in extremeprecipitation and drought over the Songhua River BasinChina during 1960ndash2013rdquo Atmospheric Research vol 157pp 137ndash152 2015

[4] H Ruimin Z Jianyun B Zhenxin Y Xiaolin W Guoqingand L Cuishan ldquoResponse of runoff to climate change in theHaihe River basinrdquo Advances in Water Resources vol 26no 1 pp 1ndash9 2015

[5] N Al Aamery J F Fox andM Snyder ldquoEvaluation of climatemodeling factors impacting the variance of streamflowrdquoJournal of Hydrology vol 542 pp 125ndash142 2016

[6] Y Chen K Takeuchi C Xu Y Chen and Z Xu ldquoRegionalclimate change and its effects on river runoff in the TarimBasin Chinardquo Hydrological Processes vol 20 no 10pp 2207ndash2216 2006

[7] R Watkins and M Kolokotroni ldquo+e London Urban HeatIslandndashupwind vegetation effects on local temperaturesrdquo2012

[8] C Xu J Zhao H Deng et al ldquoScenario-based runoff pre-diction for the kaidu river Basin of the tianshan mountainsnorthwest Chinardquo Environmental Earth Sciences vol 75no 15 2016

[9] Z-W Yan J Wang J-J Xia and J-M Feng ldquoReview ofrecent studies of the climatic effects of urbanization in ChinardquoAdvances in Climate Change Research vol 7 no 3pp 154ndash168 2016

[10] B S Bureau ldquoBeijing 2000 census datardquo 2002[11] C C Fan ldquoInterprovincial migration population redistri-

bution and regional development in China 1990 and 2000census comparisonsrdquo =e Professional Geographer vol 57no 2 pp 295ndash311 2005

[12] W Lin L Zhang D Du et al ldquoQuantification of land useland cover changes in Pearl River Delta and its impact onregional climate in summer using numerical modelingrdquoRegional Environmental Change vol 9 no 2 pp 75ndash82 2009

[13] K C Seto C E Woodcock C Song X Huang J Lu andR K Kaufmann ldquoMonitoring land-use change in the pearlriver delta using landsat TMrdquo International Journal of RemoteSensing vol 23 no 10 pp 1985ndash2004 2002

[14] S Chen W-B Li Y-D Du C-Y Mao and L ZhangldquoUrbanization effect on precipitation over the Pearl RiverDelta based on CMORPH datardquo Advances in Climate ChangeResearch vol 6 no 1 pp 16ndash22 2015

[15] R A Pielke ldquoLand use and climate changerdquo Science vol 310no 5754 pp 1625-1626 2005

[16] G Ren Y Zhou Z Chu et al ldquoUrbanization effects onobserved surface air temperature trends in North ChinardquoJournal of Climate vol 21 no 6 pp 1333ndash1348 2008

[17] PRC ldquoChinarsquos Policies and Actions for Addressing ClimateChangerdquo 2008

[18] K Trenberth P Jones P Ambenje et al ldquoObservationssurface and atmospheric climate changerdquo 2007

[19] J T Houghton IPCC (Intergovernmental Panel on ClimateChange) 1986

[20] J J McCarthy O F Canziani N A Leary D J Dokken andK S White ldquoClimate change 2001rdquo 2001

[21] Q Zhang C-Y Xu and Z Zhang ldquoObserved changes ofdroughtwetness episodes in the Pearl River basin Chinausing the standardized precipitation index and aridity indexrdquo=eoretical and Applied Climatology vol 98 no 1-2pp 89ndash99 2009

[22] Q Zhang C-Y Xu S Becker Z X Zhang Y D Chen andM Coulibaly ldquoTrends and abrupt changes of precipitationmaxima in the Pearl River basin Chinardquo Atmospheric ScienceLetters vol 10 no 2 pp 132ndash144 2009

[23] L-l Liu T Jiang and F Yuan ldquoObserved (1961ndash2007) andprojected (2011ndash2060) climate change in the pearl river ba-sinrdquo Advances in Climate Change Research vol 5 no 4pp 209ndash214 2009

[24] T Fischer M Gemmer L Luliu and S Buda ldquoTemperatureand precipitation trends and drynesswetness pattern in theZhujiang River Basin South China 1961ndash2007rdquo QuaternaryInternational vol 244 no 2 pp 138ndash148 2011

[25] Y Zhang Y Luo and L Sun ldquoQuantifying future changes inglacier melt and river runoff in the headwaters of the UrumqiRiver Chinardquo Environmental Earth Sciences vol 75 no 92016

[26] Q Zhang V P Singh J Peng Y D Chen and J Li ldquoSpatial-temporal changes of precipitation structure across the PearlRiver basin Chinardquo Journal of Hydrology vol 440ndash441pp 113ndash122 2012

[27] M Gemmer T Fischer T Jiang B Su and L L Liu ldquoTrendsin precipitation extremes in the zhujiang river basin SouthChinardquo Journal of Climate vol 24 no 3 pp 750ndash761 2011

[28] L-T Zhou and W Renguang ldquoRespective impacts of the EastAsian winter monsoon and ENSO on winter rainfall inChinardquo Journal of Geophysical Research vol 115 no D22010

[29] X Gao Y Shi R Song et al ldquoReduction of future monsoonprecipitation over China comparison between a high reso-lution RCM simulation and the driving GCMrdquo Meteorologyand Atmospheric Physics vol 100 no 1ndash4 pp 73ndash86 2008

[30] L Chen and O W Frauenfeld ldquoA comprehensive evaluationof precipitation simulations over China based on CMIP5multimodel ensemble projectionsrdquo Journal of GeophysicalResearch Atmospheres vol 119 no 10 pp 5767ndash5786 2014

[31] L Feng T Zhou B Wu T Li and J-J Luo ldquoProjection offuture precipitation change over China with a high-resolutionglobal atmospheric modelrdquoAdvances in Atmospheric Sciencesvol 28 no 2 pp 464ndash476 2011

[32] X Chong-Hai and X Ying ldquo+e projection of temperatureand precipitation over China under RCP scenarios using aCMIP5 multi-model ensemblerdquo Atmospheric and OceanicScience Letters vol 5 no 6 pp 527ndash533 2012

[33] XWang T Yang X Li P Shi and X Zhou ldquoSpatio-temporalchanges of precipitation and temperature over the Pearl Riverbasin based on CMIP5 multi-model ensemblerdquo StochasticEnvironmental Research and Risk Assessment vol 31 no 5pp 1077ndash1089 2017

[34] H Guo Q Hu and T J J O H Jiang ldquoAnnual and seasonalstreamflow responses to climate and land-cover changes inthe Poyang Lake basin Chinardquo Journal of Hydrology vol 355no 1ndash4 pp 106ndash122 2008

[35] H Hui L Hong and O Yi ldquoFlood characteristics of theXijiang River Basin in 1959ndash2008rdquo Advances in ClimateChange Research vol 3 2009

[36] Y Zhu C Guo and X Huang ldquoTrend analysis of precipi-tation extreme values of Xijiang River Basinrdquo China Hy-drology vol 32 no 2 pp 72ndash77 2012


[37] W U C Alliance ldquoOptions for improving climate modelingto assist water utility planning for climate changerdquo 2009

[38] T Fischer M Gemmer B Su T Scholten and E S SciencesldquoHydrological long-term dry and wet periods in the XijiangRiver Basin South Chinardquo Hydrology and Earth SystemSciences vol 17 no 1 pp 135ndash148 2013

[39] H B Mann ldquoNonparametric tests against trendrdquo Econo-metrica vol 13 no 3 pp 245ndash259 1945

[40] M G Kendall ldquoRank correlation methodsrdquo 1948[41] K N Krishnakumar G S L H V Prasada Rao and

C S Gopakumar ldquoRainfall trends in twentieth century overKerala Indiardquo Atmospheric Environment vol 43 no 11pp 1940ndash1944 2009

[42] A Rana C B Uvo L Bengtsson and P P Sarthi ldquoTrendanalysis for rainfall in Delhi and Mumbai Indiardquo ClimateDynamics vol 38 no 1-2 pp 45ndash56 2012

[43] S A Salman S Shahid T Ismail N bin Abd RahmanX J Wang and E S Chung ldquoUnidirectional trends in dailyrainfall extremes of Iraqrdquo =eoretical and Applied Climatol-ogy vol 134 no 3-4 pp 1165ndash1177 2018

[44] S Hempel K Frieler L Warszawski J Schewe andF Piontek ldquoA trend-preserving bias correction amp ndash theISI-MIP approachrdquo Earth System Dynamics vol 4 no 2pp 219ndash236 2013

[45] K C Abbaspour M Faramarzi S S Ghasemi and H YangldquoAssessing the impact of climate change on water resources inIranrdquo Water Resources Research vol 45 no 10 2009

[46] S A Vaghefi and K Abbaspour ldquoClimate change toolkit(CCT) user guiderdquo 2019

[47] S A Vaghefi M Keykhai F Jahanbakhshi et al ldquo+e futureof extreme climate in Iran (2045ndash2322)rdquo 2019

[48] M Kendall Rank correlation methods Charles Griffin SanFrancisco CA USA 4th edition 1975

[49] P K Sen ldquoEstimates of the regression coefficient based onkendallrsquos taurdquo Journal of the American Statistical Associationvol 63 no 324 pp 1379ndash1389 1968

[50] F Hussain G Nabi andMW Boota ldquoRainfall trend analysisby using the Mann-Kendall test amp Senrsquos slope estimates a casestudy of district Chakwal rain gauge barani area northernPunjab province Pakistanrdquo Science International vol 27no 4 2015

[51] I Ahmad D Tang T Wang M Wang andB J A I M Wagan ldquoPrecipitation trends over time usingMann-Kendall and spearmanrsquos rho tests in swat river basinPakistanrdquo Advances in Meteorology vol 10 2015

[52] A Mondal S Kundu and A Mukhopadhyay ldquoRainfall trendanalysis by Mann-Kendall test a case study of north-easternpart of Cuttack district Orissardquo International Journal ofGeology Earth and Environmental Sciences vol 2 no 1pp 70ndash78 2012

[53] S Yue P Pilon and G Cavadias ldquoPower of the Man-nndashKendall and Spearmanrsquos rho tests for detecting monotonictrends in hydrological seriesrdquo Journal of Hydrology vol 259no 1ndash4 pp 254ndash271 2002

[54] T Salmi A Maatta P Anttila T Ruoho-Airola andT Amnell ldquoMann-Kendall test and Senrsquos slope estimates forthe trend of annual datardquo 2002

[55] S Chen Y Guo Y Zheng and Y X Zheng ldquoTemporal andspatial variation of annual mean air temperature in arid andsemiarid region in northwest China over a recent 46 yearperiodrdquo Journal of Arid Land vol 2 no 2 pp 87ndash97 2010

[56] S Dai H Li H Luo Y Zhao and K Zhang ldquoChanges ofannual accumulated temperature over Southern China during

1960-2011rdquo Journal of Geographical Sciences vol 25 no 10pp 1155ndash1172 2015

[57] W J Li H C Ren J Q Zuo and H L Ren ldquoEarly summersouthern China rainfall variability and its oceanic driversrdquoClimate Dynamics vol 50 no 11-12 pp 4691ndash4705 2018

[58] W Viessman and K E Schilling Social and EnvironmentalObjectives in Water Resources Planning and Management1986

[59] H +iel ldquoA rank-invariant method of linear and polynomialregression analysis part 3rdquo 1950

[60] B Hewitson and R Crane ldquoClimate downscaling techniquesand applicationrdquo Climate Research vol 7 no 2 pp 85ndash951996

[61] Z Wang X Chen L Zhang and Y J J C H Li ldquoSpatio-temporal change characteristics of precipitation in the PearlRiver basin in recent 40 yearsrdquo Meteorology and AtmosphericPhysics vol 26 no 6 pp 71ndash75 2006

[62] Z Li F-L Zheng W-Z Liu and D-J Jiang ldquoSpatiallydownscaling GCMs outputs to project changes in extremeprecipitation and temperature events on the Loess Plateau ofChina during the 21st CenturyrdquoGlobal and Planetary Changevol 82-83 pp 65ndash73 2012

[63] F Su X Duan D Chen Z Hao and L Cuo ldquoEvaluation ofthe global climate models in the CMIP5 over the Tibetanplateaurdquo Journal of Climate vol 26 no 10 pp 3187ndash32082013


in frequent occurrence of extreme events [3] Risks ofnatural floods due to numerous environmental changes andhuman activities created huge concerns for climate changeexperts [4] Frequent extreme precipitation events causesevere floods that lead to runoff [5]

Global climate changes altered precipitation patternsand global temperature increases which could have a sig-nificant impact on the local hydrological cycle [6] +isincrease in temperature and changes in the hydrologicalcycle raised stormwater flows which are easily understoodPrecipitation patterns over the urban areas are affected bychanges in surface albedo and vegetation cover All thesefactors increase runoff due to retardation of the infiltrationand evapotranspiration process [7] +e statistical down-scaling model (SDSM) and Statistical Analog ResamplingScheme (STARS) were used to downscale the GCM outputsfor projecting the future climate scenarios and performedwell in simulating temperature and precipitation [8]

China has very swift economic growth in the past fewdecades Urbanization which leads to significant impacts onland-use changes was an 85 million hectare square meter in2013 According to the National Bureau Statistics of Chinathe municipal population exceeds 50 in recent years andthis will be over 80 in 2050 by Yan et al [9] +esouthcentral and southwestern provinces of mainland Chinareceived the most prominent donors of migrants from 1995to 2000 [10 11] Pearl River Delta has not been a region ofrapid land conversion historically for hundreds of years butthe government directives in early 1980s regarding eco-nomic growth which directly upgraded the living standardsand urbanization rate of 300 in the delta have seriousimpacts on various climate observations [12 13] Since the1980s rapid economic growth and policy change turned thePearl River Delta (PRD) region as the fastest populatedregion [13] Water-Energy-Food Nexus alters as a result ofmigration from urban to rural areas owing to changes in theradiation process +e anthropogenic aerosols carbonemissions and high-rise buildings affect the air quality localweather and climate [14]+e anthropogenic forcing mainlyincludes the emissions of greenhouse gases (GHGs) as wellas land-useland-cover changes [15] Ren et al [16] pre-sented evidence for the rapid urbanization effect 005degC perdecade increase in temperature is recorded as a result ofurbanization in mainland China

China observed an increase of 11degC from 1908 to 2007 inaverage surface temperature [2 17] Extreme weather eventshave great impacts on the ecosystem and society Variousstudies were conducted throughout the world to analyze thenature of extreme events and concluded that future climatechange will increase the intensity and frequency of suchevents [18]

Recently changes in precipitation trends have attractedthe researcherrsquos attention Southern China observed a30ndash50 increase in precipitation in the winter season(December January and February) from 1900 to 1999[19 20] Standardized Precipitation Index (SPI) trendsacross the Pearl River Basin for the monsoon characterizedby decreasing SPI shows that dry days govern major parts ofthe Pearl River Basin while winter (December-February) is

characterized by increasing SPI trends [21] Variations of theannual and seasonal rainfall are not significant at gt95confidence level However substantial negative trends canbe observed in the number of wet days [22]

Liu et al [23] detected an increase of +18degC in annual airtemperature from 1961 to 2007 at Pearl River Basin Fischeret al [24] applied the MannndashKendall test to daily meantemperature for 157 stations and found significant positivetrends of annual mean temperature +e study also sum-marized that the whole basin observed positive trends inannual and monthly mean temperatures however thetemperature increased less in summer than in winter Zhanget al [25]applied the SWAT model to GCMsrsquo outputs inUrumqi River and both temperature and precipitation showincrease in near and far future

+e long-term average precipitation of the Pearl RiverBasin is nearly 1500mm Average of 2mm per decade isobserved in the changing rate in annual average precipita-tion by evaluating 42 rainfall stations 110 rainy days with 14days per decade is the changing magnitude for long-termannual average precipitation while 135mmday is the long-term annual average rainfall intensity with 014mmday perdecade changing magnitude [26]

Gemmer et al [27] also concluded their findings for 192stations (1961ndash2007) for annual monthly and daily sums inthe Pearl River Basin that autumn precipitation observeddeclined trends and spring summer and winter rainfall haveinclined trends +e same findings were supported by manyother researchers in their studies [22] +e East-Asianmonsoon plays a key role in local rainfall trends summa-rized by [28] that strong winter monsoon with northerlywinds is governed by declined trends in winter season oversouthern China

Gao et al [29] recommended that high-resolutionmodels are better to examine future climate projections overChina and East Asia Chen et al [30] evaluated historicalprecipitation variability over 21st century CMIP5 archiveestimates which are put into context based on the 20thcentury biases and concluded that CMIP5 models canproduce better spatial patterns over CMIP3 Feng et al [31]studied future projections based on the global AGCM overChina and concluded that annual precipitation is close to thestation data +e regional mean precipitation will increase innorthern regions greater than southern regions in Chinabased on the projections of 11 climate models under rep-resentative concentration pathway (RCP) scenarios [32]Similarly the Pearl River Basin will likely be inclined trendsin precipitation under RCP26 and RCP45 scenarioswhereas declined trends under RCP85 [33]

Guo et al [34] summarized that climate plays a key rolein changing basin hydrology streamflow in the Xijiang RiverBasin China +e Xijiang River Basin has the main tributaryof the Pearl River Basin which lies in the subtropical regionof South China +e Pearl River Basin is the third largestriver basin of China with more than 100 million peopleresiding Since 1990 the Xijiang River Basin observed fre-quent flood disasters due to heavy storm events [35] Aslightly increasing trend was observed historically(1951ndash2010) during the dry season of the Xijiang River Basin











xi f ti( 1113857 + εi (1)


S 1113944nminus1

i11113944

n

ji+1sgn xj minus xi1113872 1113873 (2)




0 if xj minus xi 0


⎧⎪⎪⎪⎨

⎪⎪⎪⎩

(3)



E(S) 0 (4)

Var (S) 118


i1ti ti minus 1( 1113857 2ti + 5( 1113857⎡⎣ ⎤⎦

(5)


Z

S minus 1VAR(S)

1113968 if Sgt 0

0 if S 0

S + 1VAR(S)

1113968 if Slt 0

⎧⎪⎪⎪⎪⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎪⎪⎪⎪⎩

(6)


0 225 450 675 9001125kilometers


Low 0

21deg0prime0PrimeN

22deg0prime0PrimeN

23deg0prime0PrimeN

24deg0prime0PrimeN

25deg0prime0PrimeN

26deg0prime0PrimeN

21deg0prime0PrimeN

22deg0prime0PrimeN

23deg0prime0PrimeN

24deg0prime0PrimeN

25deg0prime0PrimeN

26deg0prime0PrimeN



0deg0prime0Prime

15deg0prime0PrimeN

30deg0prime0PrimeN

45deg0prime0PrimeN


0deg0prime0Prime

15deg0prime0PrimeN

30deg0prime0PrimeN

45deg0prime0PrimeN







mjk yj minus yk

j minus k (7)


111deg0prime0PrimeE

111deg0prime0PrimeE

108deg0prime0PrimeE

108deg0prime0PrimeE

105deg0prime0PrimeE

105deg0prime0PrimeE

102deg0prime0PrimeE

102deg0prime0PrimeE





0 190 380 570 76095kilometers

Xijiang basinvalue

High 2844

Low 0

Weather stations



Qmed


12

m(n2)1113872 +m(n+22)1113873 for n is even

⎧⎪⎪⎪⎨

⎪⎪⎪⎩

(8)


3 Results
















0

200

400

600

800

1000

1200

1400

1600

1800

Prec

ipita

tion

(mm

)

1970 1980 1990 2000 20101960Time (year)






100000

90000

80000

70000

60000

50000

Mon

soon

40000

30000

20000

10000

0001950 1960 1970 1980 1990 2000 2010 2020

Year


(a)

25000

20000

15000

Win

ter

10000

5000

0001950 1960 1970 1980 1990 2000 2010 2020

Year


(b)60000

50000

Prem

onso

on

40000

30000

20000

10000

0001950 1960 1970 1980 1990 2000 2010 2020

Year


(c)

Postm

onso

on

45000

30000

35000

40000

20000

25000

10000

15000

5000

0001950 1960 1970 1980 1990 2000 2010 2020

Year


(d)180000

160000

140000

120000

100000

80000

60000

40000

20000

000

Ann

ual

1950 1960 1970 1980Year

1990 2000 2010 2020


(e)














000

50000

100000

150000

200000

250000

300000A

nnua

l

1960 1970 1980 1990 2000 2010 20201950Year

(a)


1960 1970 1980 1990 2000 2010 20201950Year

000

20000

40000

60000

80000

100000

120000

140000

160000

Mon

soon

(b)


000

5000

10000

15000

20000

25000

30000

35000

40000

Win

ter

1960 1970 1980 1990 2000 2010 20201950Year

(c)


000

20000

40000

60000

80000

100000

120000

140000

160000Pr

emon

soon

1960 1970 1980 1990 2000 2010 20201950Year

(d)


000

10000

20000

30000

40000

50000

60000

Postm

onso

on

1960 1970 1980 1990 2000 2010 20201950Year

(e)




000

20000

40000

60000

80000

100000

120000

140000A

nnua

l

1960 1970 1980 1990 2000 2010 20201950Year

(a)


000

10000

20000

30000

40000

50000

60000

70000

80000

90000

Mon

soon

1960 1970 1980 1990 2000 2010 20201950Year

(b)


000

2000

4000

6000

8000

10000

12000

Win

ter

1960 1970 1980 1990 2000 2010 20201950Year

(c)


000

5000

10000

15000

20000

25000

30000

35000

40000Pr

emon

soon

1960 1970 1980 1990 2000 2010 20201950Year

(d)


000

10000

20000

30000

40000

50000

60000

Postm

onso

on

1960 1970 1980 1990 2000 2010 20201950Year

(e)




4 Discussion




500

700

900

1100

1300

1500

1700

1900

2100

Aver

age a

nnua

l pre

cipi

tatio

n (m

m)

1960 1970 1980 1990 20001950Year




GFDL-ESM2M


Had-GEM2-ES


IPSL-CM5A-LR


MIROC


NoerESM1-M






GFDL-ESM2M


Had-GEM2-ES


IPSL-CM5A-LR


MIROC


NoerESM1-M









0

500

1000

1500

2000

250019

40

1950

1960

1970

1980

1990

2000

2010

Ann

ual

Year

0

500

1000

1500

2000

2500

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

1940

1950

1960

1970

1980

1990

2000

2010

Ann

ual

Year

01020304050607080

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0102030405060708090

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

20

40

60

80

100

120

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

20

40

60

80

100

120

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

1940

1950

1960

1970

1980

1990

2000

2010

Ann

ual

Year

0

500

1000

1500

2000

2500

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0200400600800

100012001400160018002000

Ann

ual

Year

0

500

1000

1500

2000

2500

1940

1950

1960

1970

1980

1990

2000

2010

Ann

ual

MIR

OC

Noe

rESM

1-M

Year

020406080

100120140160180200

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0200400600800

100012001400160018002000

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

Ann

ual

Year

0200400600800

100012001400160018002000

1940

1950

1960

1970

1980

1990

2000

2010

Ann

ual

Year

0

500

1000

1500

2000

2500

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

Ann

ual

Year

Historical

GFD

L-ES

M2M

Had

-GEM

2ES

IPSL

-CM

5A-L

R

































GFDL-ESM2M


Had-GEM2-ES


IPSL-CM5A-LR


MIROC


NoerESM1-M



AnnualMonsoonWinter



0200400600800

1000120014001600

Prec

ipita

tion

(mm

)




5 Conclusion



Data Availability




Acknowledgments


RCP26RCP45

RCP60RCP85

1200

1250

1300

1350

1400

1450

1500

1550

1600

1650

1700

Prec

ipita

tion

(mm

)






References












































































xi f ti( 1113857 + εi (1)


S 1113944nminus1

i11113944

n

ji+1sgn xj minus xi1113872 1113873 (2)




0 if xj minus xi 0


⎧⎪⎪⎪⎨

⎪⎪⎪⎩

(3)



E(S) 0 (4)

Var (S) 118


i1ti ti minus 1( 1113857 2ti + 5( 1113857⎡⎣ ⎤⎦

(5)


Z

S minus 1VAR(S)

1113968 if Sgt 0

0 if S 0

S + 1VAR(S)

1113968 if Slt 0

⎧⎪⎪⎪⎪⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎪⎪⎪⎪⎩

(6)


0 225 450 675 9001125kilometers


Low 0

21deg0prime0PrimeN

22deg0prime0PrimeN

23deg0prime0PrimeN

24deg0prime0PrimeN

25deg0prime0PrimeN

26deg0prime0PrimeN

21deg0prime0PrimeN

22deg0prime0PrimeN

23deg0prime0PrimeN

24deg0prime0PrimeN

25deg0prime0PrimeN

26deg0prime0PrimeN



0deg0prime0Prime

15deg0prime0PrimeN

30deg0prime0PrimeN

45deg0prime0PrimeN


0deg0prime0Prime

15deg0prime0PrimeN

30deg0prime0PrimeN

45deg0prime0PrimeN







mjk yj minus yk

j minus k (7)


111deg0prime0PrimeE

111deg0prime0PrimeE

108deg0prime0PrimeE

108deg0prime0PrimeE

105deg0prime0PrimeE

105deg0prime0PrimeE

102deg0prime0PrimeE

102deg0prime0PrimeE





0 190 380 570 76095kilometers

Xijiang basinvalue

High 2844

Low 0

Weather stations



Qmed


12

m(n2)1113872 +m(n+22)1113873 for n is even

⎧⎪⎪⎪⎨

⎪⎪⎪⎩

(8)


3 Results
















0

200

400

600

800

1000

1200

1400

1600

1800

Prec

ipita

tion

(mm

)

1970 1980 1990 2000 20101960Time (year)






100000

90000

80000

70000

60000

50000

Mon

soon

40000

30000

20000

10000

0001950 1960 1970 1980 1990 2000 2010 2020

Year


(a)

25000

20000

15000

Win

ter

10000

5000

0001950 1960 1970 1980 1990 2000 2010 2020

Year


(b)60000

50000

Prem

onso

on

40000

30000

20000

10000

0001950 1960 1970 1980 1990 2000 2010 2020

Year


(c)

Postm

onso

on

45000

30000

35000

40000

20000

25000

10000

15000

5000

0001950 1960 1970 1980 1990 2000 2010 2020

Year


(d)180000

160000

140000

120000

100000

80000

60000

40000

20000

000

Ann

ual

1950 1960 1970 1980Year

1990 2000 2010 2020


(e)














000

50000

100000

150000

200000

250000

300000A

nnua

l

1960 1970 1980 1990 2000 2010 20201950Year

(a)


1960 1970 1980 1990 2000 2010 20201950Year

000

20000

40000

60000

80000

100000

120000

140000

160000

Mon

soon

(b)


000

5000

10000

15000

20000

25000

30000

35000

40000

Win

ter

1960 1970 1980 1990 2000 2010 20201950Year

(c)


000

20000

40000

60000

80000

100000

120000

140000

160000Pr

emon

soon

1960 1970 1980 1990 2000 2010 20201950Year

(d)


000

10000

20000

30000

40000

50000

60000

Postm

onso

on

1960 1970 1980 1990 2000 2010 20201950Year

(e)




000

20000

40000

60000

80000

100000

120000

140000A

nnua

l

1960 1970 1980 1990 2000 2010 20201950Year

(a)


000

10000

20000

30000

40000

50000

60000

70000

80000

90000

Mon

soon

1960 1970 1980 1990 2000 2010 20201950Year

(b)


000

2000

4000

6000

8000

10000

12000

Win

ter

1960 1970 1980 1990 2000 2010 20201950Year

(c)


000

5000

10000

15000

20000

25000

30000

35000

40000Pr

emon

soon

1960 1970 1980 1990 2000 2010 20201950Year

(d)


000

10000

20000

30000

40000

50000

60000

Postm

onso

on

1960 1970 1980 1990 2000 2010 20201950Year

(e)




4 Discussion




500

700

900

1100

1300

1500

1700

1900

2100

Aver

age a

nnua

l pre

cipi

tatio

n (m

m)

1960 1970 1980 1990 20001950Year




GFDL-ESM2M


Had-GEM2-ES


IPSL-CM5A-LR


MIROC


NoerESM1-M






GFDL-ESM2M


Had-GEM2-ES


IPSL-CM5A-LR


MIROC


NoerESM1-M









0

500

1000

1500

2000

250019

40

1950

1960

1970

1980

1990

2000

2010

Ann

ual

Year

0

500

1000

1500

2000

2500

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

1940

1950

1960

1970

1980

1990

2000

2010

Ann

ual

Year

01020304050607080

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0102030405060708090

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

20

40

60

80

100

120

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

20

40

60

80

100

120

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

1940

1950

1960

1970

1980

1990

2000

2010

Ann

ual

Year

0

500

1000

1500

2000

2500

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0200400600800

100012001400160018002000

Ann

ual

Year

0

500

1000

1500

2000

2500

1940

1950

1960

1970

1980

1990

2000

2010

Ann

ual

MIR

OC

Noe

rESM

1-M

Year

020406080

100120140160180200

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0200400600800

100012001400160018002000

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

Ann

ual

Year

0200400600800

100012001400160018002000

1940

1950

1960

1970

1980

1990

2000

2010

Ann

ual

Year

0

500

1000

1500

2000

2500

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

Ann

ual

Year

Historical

GFD

L-ES

M2M

Had

-GEM

2ES

IPSL

-CM

5A-L

R

































GFDL-ESM2M


Had-GEM2-ES


IPSL-CM5A-LR


MIROC


NoerESM1-M



AnnualMonsoonWinter



0200400600800

1000120014001600

Prec

ipita

tion

(mm

)




5 Conclusion



Data Availability




Acknowledgments


RCP26RCP45

RCP60RCP85

1200

1250

1300

1350

1400

1450

1500

1550

1600

1650

1700

Prec

ipita

tion

(mm

)






References



































































E(S) 0 (4)

Var (S) 118


i1ti ti minus 1( 1113857 2ti + 5( 1113857⎡⎣ ⎤⎦

(5)


Z

S minus 1VAR(S)

1113968 if Sgt 0

0 if S 0

S + 1VAR(S)

1113968 if Slt 0

⎧⎪⎪⎪⎪⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎪⎪⎪⎪⎩

(6)


0 225 450 675 9001125kilometers


Low 0

21deg0prime0PrimeN

22deg0prime0PrimeN

23deg0prime0PrimeN

24deg0prime0PrimeN

25deg0prime0PrimeN

26deg0prime0PrimeN

21deg0prime0PrimeN

22deg0prime0PrimeN

23deg0prime0PrimeN

24deg0prime0PrimeN

25deg0prime0PrimeN

26deg0prime0PrimeN



0deg0prime0Prime

15deg0prime0PrimeN

30deg0prime0PrimeN

45deg0prime0PrimeN


0deg0prime0Prime

15deg0prime0PrimeN

30deg0prime0PrimeN

45deg0prime0PrimeN







mjk yj minus yk

j minus k (7)


111deg0prime0PrimeE

111deg0prime0PrimeE

108deg0prime0PrimeE

108deg0prime0PrimeE

105deg0prime0PrimeE

105deg0prime0PrimeE

102deg0prime0PrimeE

102deg0prime0PrimeE





0 190 380 570 76095kilometers

Xijiang basinvalue

High 2844

Low 0

Weather stations



Qmed


12

m(n2)1113872 +m(n+22)1113873 for n is even

⎧⎪⎪⎪⎨

⎪⎪⎪⎩

(8)


3 Results
















0

200

400

600

800

1000

1200

1400

1600

1800

Prec

ipita

tion

(mm

)

1970 1980 1990 2000 20101960Time (year)






100000

90000

80000

70000

60000

50000

Mon

soon

40000

30000

20000

10000

0001950 1960 1970 1980 1990 2000 2010 2020

Year


(a)

25000

20000

15000

Win

ter

10000

5000

0001950 1960 1970 1980 1990 2000 2010 2020

Year


(b)60000

50000

Prem

onso

on

40000

30000

20000

10000

0001950 1960 1970 1980 1990 2000 2010 2020

Year


(c)

Postm

onso

on

45000

30000

35000

40000

20000

25000

10000

15000

5000

0001950 1960 1970 1980 1990 2000 2010 2020

Year


(d)180000

160000

140000

120000

100000

80000

60000

40000

20000

000

Ann

ual

1950 1960 1970 1980Year

1990 2000 2010 2020


(e)














000

50000

100000

150000

200000

250000

300000A

nnua

l

1960 1970 1980 1990 2000 2010 20201950Year

(a)


1960 1970 1980 1990 2000 2010 20201950Year

000

20000

40000

60000

80000

100000

120000

140000

160000

Mon

soon

(b)


000

5000

10000

15000

20000

25000

30000

35000

40000

Win

ter

1960 1970 1980 1990 2000 2010 20201950Year

(c)


000

20000

40000

60000

80000

100000

120000

140000

160000Pr

emon

soon

1960 1970 1980 1990 2000 2010 20201950Year

(d)


000

10000

20000

30000

40000

50000

60000

Postm

onso

on

1960 1970 1980 1990 2000 2010 20201950Year

(e)




000

20000

40000

60000

80000

100000

120000

140000A

nnua

l

1960 1970 1980 1990 2000 2010 20201950Year

(a)


000

10000

20000

30000

40000

50000

60000

70000

80000

90000

Mon

soon

1960 1970 1980 1990 2000 2010 20201950Year

(b)


000

2000

4000

6000

8000

10000

12000

Win

ter

1960 1970 1980 1990 2000 2010 20201950Year

(c)


000

5000

10000

15000

20000

25000

30000

35000

40000Pr

emon

soon

1960 1970 1980 1990 2000 2010 20201950Year

(d)


000

10000

20000

30000

40000

50000

60000

Postm

onso

on

1960 1970 1980 1990 2000 2010 20201950Year

(e)




4 Discussion




500

700

900

1100

1300

1500

1700

1900

2100

Aver

age a

nnua

l pre

cipi

tatio

n (m

m)

1960 1970 1980 1990 20001950Year




GFDL-ESM2M


Had-GEM2-ES


IPSL-CM5A-LR


MIROC


NoerESM1-M






GFDL-ESM2M


Had-GEM2-ES


IPSL-CM5A-LR


MIROC


NoerESM1-M









0

500

1000

1500

2000

250019

40

1950

1960

1970

1980

1990

2000

2010

Ann

ual

Year

0

500

1000

1500

2000

2500

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

1940

1950

1960

1970

1980

1990

2000

2010

Ann

ual

Year

01020304050607080

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0102030405060708090

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

20

40

60

80

100

120

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

20

40

60

80

100

120

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

1940

1950

1960

1970

1980

1990

2000

2010

Ann

ual

Year

0

500

1000

1500

2000

2500

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0200400600800

100012001400160018002000

Ann

ual

Year

0

500

1000

1500

2000

2500

1940

1950

1960

1970

1980

1990

2000

2010

Ann

ual

MIR

OC

Noe

rESM

1-M

Year

020406080

100120140160180200

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0200400600800

100012001400160018002000

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

Ann

ual

Year

0200400600800

100012001400160018002000

1940

1950

1960

1970

1980

1990

2000

2010

Ann

ual

Year

0

500

1000

1500

2000

2500

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

Ann

ual

Year

Historical

GFD

L-ES

M2M

Had

-GEM

2ES

IPSL

-CM

5A-L

R

































GFDL-ESM2M


Had-GEM2-ES


IPSL-CM5A-LR


MIROC


NoerESM1-M



AnnualMonsoonWinter



0200400600800

1000120014001600

Prec

ipita

tion

(mm

)




5 Conclusion



Data Availability




Acknowledgments


RCP26RCP45

RCP60RCP85

1200

1250

1300

1350

1400

1450

1500

1550

1600

1650

1700

Prec

ipita

tion

(mm

)






References






































































mjk yj minus yk

j minus k (7)


111deg0prime0PrimeE

111deg0prime0PrimeE

108deg0prime0PrimeE

108deg0prime0PrimeE

105deg0prime0PrimeE

105deg0prime0PrimeE

102deg0prime0PrimeE

102deg0prime0PrimeE





0 190 380 570 76095kilometers

Xijiang basinvalue

High 2844

Low 0

Weather stations



Qmed


12

m(n2)1113872 +m(n+22)1113873 for n is even

⎧⎪⎪⎪⎨

⎪⎪⎪⎩

(8)


3 Results
















0

200

400

600

800

1000

1200

1400

1600

1800

Prec

ipita

tion

(mm

)

1970 1980 1990 2000 20101960Time (year)






100000

90000

80000

70000

60000

50000

Mon

soon

40000

30000

20000

10000

0001950 1960 1970 1980 1990 2000 2010 2020

Year


(a)

25000

20000

15000

Win

ter

10000

5000

0001950 1960 1970 1980 1990 2000 2010 2020

Year


(b)60000

50000

Prem

onso

on

40000

30000

20000

10000

0001950 1960 1970 1980 1990 2000 2010 2020

Year


(c)

Postm

onso

on

45000

30000

35000

40000

20000

25000

10000

15000

5000

0001950 1960 1970 1980 1990 2000 2010 2020

Year


(d)180000

160000

140000

120000

100000

80000

60000

40000

20000

000

Ann

ual

1950 1960 1970 1980Year

1990 2000 2010 2020


(e)














000

50000

100000

150000

200000

250000

300000A

nnua

l

1960 1970 1980 1990 2000 2010 20201950Year

(a)


1960 1970 1980 1990 2000 2010 20201950Year

000

20000

40000

60000

80000

100000

120000

140000

160000

Mon

soon

(b)


000

5000

10000

15000

20000

25000

30000

35000

40000

Win

ter

1960 1970 1980 1990 2000 2010 20201950Year

(c)


000

20000

40000

60000

80000

100000

120000

140000

160000Pr

emon

soon

1960 1970 1980 1990 2000 2010 20201950Year

(d)


000

10000

20000

30000

40000

50000

60000

Postm

onso

on

1960 1970 1980 1990 2000 2010 20201950Year

(e)




000

20000

40000

60000

80000

100000

120000

140000A

nnua

l

1960 1970 1980 1990 2000 2010 20201950Year

(a)


000

10000

20000

30000

40000

50000

60000

70000

80000

90000

Mon

soon

1960 1970 1980 1990 2000 2010 20201950Year

(b)


000

2000

4000

6000

8000

10000

12000

Win

ter

1960 1970 1980 1990 2000 2010 20201950Year

(c)


000

5000

10000

15000

20000

25000

30000

35000

40000Pr

emon

soon

1960 1970 1980 1990 2000 2010 20201950Year

(d)


000

10000

20000

30000

40000

50000

60000

Postm

onso

on

1960 1970 1980 1990 2000 2010 20201950Year

(e)




4 Discussion




500

700

900

1100

1300

1500

1700

1900

2100

Aver

age a

nnua

l pre

cipi

tatio

n (m

m)

1960 1970 1980 1990 20001950Year




GFDL-ESM2M


Had-GEM2-ES


IPSL-CM5A-LR


MIROC


NoerESM1-M






GFDL-ESM2M


Had-GEM2-ES


IPSL-CM5A-LR


MIROC


NoerESM1-M









0

500

1000

1500

2000

250019

40

1950

1960

1970

1980

1990

2000

2010

Ann

ual

Year

0

500

1000

1500

2000

2500

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

1940

1950

1960

1970

1980

1990

2000

2010

Ann

ual

Year

01020304050607080

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0102030405060708090

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

20

40

60

80

100

120

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

20

40

60

80

100

120

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

1940

1950

1960

1970

1980

1990

2000

2010

Ann

ual

Year

0

500

1000

1500

2000

2500

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0200400600800

100012001400160018002000

Ann

ual

Year

0

500

1000

1500

2000

2500

1940

1950

1960

1970

1980

1990

2000

2010

Ann

ual

MIR

OC

Noe

rESM

1-M

Year

020406080

100120140160180200

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0200400600800

100012001400160018002000

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

Ann

ual

Year

0200400600800

100012001400160018002000

1940

1950

1960

1970

1980

1990

2000

2010

Ann

ual

Year

0

500

1000

1500

2000

2500

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

Ann

ual

Year

Historical

GFD

L-ES

M2M

Had

-GEM

2ES

IPSL

-CM

5A-L

R

































GFDL-ESM2M


Had-GEM2-ES


IPSL-CM5A-LR


MIROC


NoerESM1-M



AnnualMonsoonWinter



0200400600800

1000120014001600

Prec

ipita

tion

(mm

)




5 Conclusion



Data Availability




Acknowledgments


RCP26RCP45

RCP60RCP85

1200

1250

1300

1350

1400

1450

1500

1550

1600

1650

1700

Prec

ipita

tion

(mm

)






References



































































Qmed


12

m(n2)1113872 +m(n+22)1113873 for n is even

⎧⎪⎪⎪⎨

⎪⎪⎪⎩

(8)


3 Results
















0

200

400

600

800

1000

1200

1400

1600

1800

Prec

ipita

tion

(mm

)

1970 1980 1990 2000 20101960Time (year)






100000

90000

80000

70000

60000

50000

Mon

soon

40000

30000

20000

10000

0001950 1960 1970 1980 1990 2000 2010 2020

Year


(a)

25000

20000

15000

Win

ter

10000

5000

0001950 1960 1970 1980 1990 2000 2010 2020

Year


(b)60000

50000

Prem

onso

on

40000

30000

20000

10000

0001950 1960 1970 1980 1990 2000 2010 2020

Year


(c)

Postm

onso

on

45000

30000

35000

40000

20000

25000

10000

15000

5000

0001950 1960 1970 1980 1990 2000 2010 2020

Year


(d)180000

160000

140000

120000

100000

80000

60000

40000

20000

000

Ann

ual

1950 1960 1970 1980Year

1990 2000 2010 2020


(e)














000

50000

100000

150000

200000

250000

300000A

nnua

l

1960 1970 1980 1990 2000 2010 20201950Year

(a)


1960 1970 1980 1990 2000 2010 20201950Year

000

20000

40000

60000

80000

100000

120000

140000

160000

Mon

soon

(b)


000

5000

10000

15000

20000

25000

30000

35000

40000

Win

ter

1960 1970 1980 1990 2000 2010 20201950Year

(c)


000

20000

40000

60000

80000

100000

120000

140000

160000Pr

emon

soon

1960 1970 1980 1990 2000 2010 20201950Year

(d)


000

10000

20000

30000

40000

50000

60000

Postm

onso

on

1960 1970 1980 1990 2000 2010 20201950Year

(e)




000

20000

40000

60000

80000

100000

120000

140000A

nnua

l

1960 1970 1980 1990 2000 2010 20201950Year

(a)


000

10000

20000

30000

40000

50000

60000

70000

80000

90000

Mon

soon

1960 1970 1980 1990 2000 2010 20201950Year

(b)


000

2000

4000

6000

8000

10000

12000

Win

ter

1960 1970 1980 1990 2000 2010 20201950Year

(c)


000

5000

10000

15000

20000

25000

30000

35000

40000Pr

emon

soon

1960 1970 1980 1990 2000 2010 20201950Year

(d)


000

10000

20000

30000

40000

50000

60000

Postm

onso

on

1960 1970 1980 1990 2000 2010 20201950Year

(e)




4 Discussion




500

700

900

1100

1300

1500

1700

1900

2100

Aver

age a

nnua

l pre

cipi

tatio

n (m

m)

1960 1970 1980 1990 20001950Year




GFDL-ESM2M


Had-GEM2-ES


IPSL-CM5A-LR


MIROC


NoerESM1-M






GFDL-ESM2M


Had-GEM2-ES


IPSL-CM5A-LR


MIROC


NoerESM1-M









0

500

1000

1500

2000

250019

40

1950

1960

1970

1980

1990

2000

2010

Ann

ual

Year

0

500

1000

1500

2000

2500

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

1940

1950

1960

1970

1980

1990

2000

2010

Ann

ual

Year

01020304050607080

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0102030405060708090

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

20

40

60

80

100

120

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

20

40

60

80

100

120

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

1940

1950

1960

1970

1980

1990

2000

2010

Ann

ual

Year

0

500

1000

1500

2000

2500

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0200400600800

100012001400160018002000

Ann

ual

Year

0

500

1000

1500

2000

2500

1940

1950

1960

1970

1980

1990

2000

2010

Ann

ual

MIR

OC

Noe

rESM

1-M

Year

020406080

100120140160180200

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0200400600800

100012001400160018002000

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

Ann

ual

Year

0200400600800

100012001400160018002000

1940

1950

1960

1970

1980

1990

2000

2010

Ann

ual

Year

0

500

1000

1500

2000

2500

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

Ann

ual

Year

Historical

GFD

L-ES

M2M

Had

-GEM

2ES

IPSL

-CM

5A-L

R

































GFDL-ESM2M


Had-GEM2-ES


IPSL-CM5A-LR


MIROC


NoerESM1-M



AnnualMonsoonWinter



0200400600800

1000120014001600

Prec

ipita

tion

(mm

)




5 Conclusion



Data Availability




Acknowledgments


RCP26RCP45

RCP60RCP85

1200

1250

1300

1350

1400

1450

1500

1550

1600

1650

1700

Prec

ipita

tion

(mm

)






References








































































0

200

400

600

800

1000

1200

1400

1600

1800

Prec

ipita

tion

(mm

)

1970 1980 1990 2000 20101960Time (year)






100000

90000

80000

70000

60000

50000

Mon

soon

40000

30000

20000

10000

0001950 1960 1970 1980 1990 2000 2010 2020

Year


(a)

25000

20000

15000

Win

ter

10000

5000

0001950 1960 1970 1980 1990 2000 2010 2020

Year


(b)60000

50000

Prem

onso

on

40000

30000

20000

10000

0001950 1960 1970 1980 1990 2000 2010 2020

Year


(c)

Postm

onso

on

45000

30000

35000

40000

20000

25000

10000

15000

5000

0001950 1960 1970 1980 1990 2000 2010 2020

Year


(d)180000

160000

140000

120000

100000

80000

60000

40000

20000

000

Ann

ual

1950 1960 1970 1980Year

1990 2000 2010 2020


(e)














000

50000

100000

150000

200000

250000

300000A

nnua

l

1960 1970 1980 1990 2000 2010 20201950Year

(a)


1960 1970 1980 1990 2000 2010 20201950Year

000

20000

40000

60000

80000

100000

120000

140000

160000

Mon

soon

(b)


000

5000

10000

15000

20000

25000

30000

35000

40000

Win

ter

1960 1970 1980 1990 2000 2010 20201950Year

(c)


000

20000

40000

60000

80000

100000

120000

140000

160000Pr

emon

soon

1960 1970 1980 1990 2000 2010 20201950Year

(d)


000

10000

20000

30000

40000

50000

60000

Postm

onso

on

1960 1970 1980 1990 2000 2010 20201950Year

(e)




000

20000

40000

60000

80000

100000

120000

140000A

nnua

l

1960 1970 1980 1990 2000 2010 20201950Year

(a)


000

10000

20000

30000

40000

50000

60000

70000

80000

90000

Mon

soon

1960 1970 1980 1990 2000 2010 20201950Year

(b)


000

2000

4000

6000

8000

10000

12000

Win

ter

1960 1970 1980 1990 2000 2010 20201950Year

(c)


000

5000

10000

15000

20000

25000

30000

35000

40000Pr

emon

soon

1960 1970 1980 1990 2000 2010 20201950Year

(d)


000

10000

20000

30000

40000

50000

60000

Postm

onso

on

1960 1970 1980 1990 2000 2010 20201950Year

(e)




4 Discussion




500

700

900

1100

1300

1500

1700

1900

2100

Aver

age a

nnua

l pre

cipi

tatio

n (m

m)

1960 1970 1980 1990 20001950Year




GFDL-ESM2M


Had-GEM2-ES


IPSL-CM5A-LR


MIROC


NoerESM1-M






GFDL-ESM2M


Had-GEM2-ES


IPSL-CM5A-LR


MIROC


NoerESM1-M









0

500

1000

1500

2000

250019

40

1950

1960

1970

1980

1990

2000

2010

Ann

ual

Year

0

500

1000

1500

2000

2500

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

1940

1950

1960

1970

1980

1990

2000

2010

Ann

ual

Year

01020304050607080

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0102030405060708090

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

20

40

60

80

100

120

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

20

40

60

80

100

120

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

1940

1950

1960

1970

1980

1990

2000

2010

Ann

ual

Year

0

500

1000

1500

2000

2500

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0200400600800

100012001400160018002000

Ann

ual

Year

0

500

1000

1500

2000

2500

1940

1950

1960

1970

1980

1990

2000

2010

Ann

ual

MIR

OC

Noe

rESM

1-M

Year

020406080

100120140160180200

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0200400600800

100012001400160018002000

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

Ann

ual

Year

0200400600800

100012001400160018002000

1940

1950

1960

1970

1980

1990

2000

2010

Ann

ual

Year

0

500

1000

1500

2000

2500

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

Ann

ual

Year

Historical

GFD

L-ES

M2M

Had

-GEM

2ES

IPSL

-CM

5A-L

R

































GFDL-ESM2M


Had-GEM2-ES


IPSL-CM5A-LR


MIROC


NoerESM1-M



AnnualMonsoonWinter



0200400600800

1000120014001600

Prec

ipita

tion

(mm

)




5 Conclusion



Data Availability




Acknowledgments


RCP26RCP45

RCP60RCP85

1200

1250

1300

1350

1400

1450

1500

1550

1600

1650

1700

Prec

ipita

tion

(mm

)






References






































































100000

90000

80000

70000

60000

50000

Mon

soon

40000

30000

20000

10000

0001950 1960 1970 1980 1990 2000 2010 2020

Year


(a)

25000

20000

15000

Win

ter

10000

5000

0001950 1960 1970 1980 1990 2000 2010 2020

Year


(b)60000

50000

Prem

onso

on

40000

30000

20000

10000

0001950 1960 1970 1980 1990 2000 2010 2020

Year


(c)

Postm

onso

on

45000

30000

35000

40000

20000

25000

10000

15000

5000

0001950 1960 1970 1980 1990 2000 2010 2020

Year


(d)180000

160000

140000

120000

100000

80000

60000

40000

20000

000

Ann

ual

1950 1960 1970 1980Year

1990 2000 2010 2020


(e)














000

50000

100000

150000

200000

250000

300000A

nnua

l

1960 1970 1980 1990 2000 2010 20201950Year

(a)


1960 1970 1980 1990 2000 2010 20201950Year

000

20000

40000

60000

80000

100000

120000

140000

160000

Mon

soon

(b)


000

5000

10000

15000

20000

25000

30000

35000

40000

Win

ter

1960 1970 1980 1990 2000 2010 20201950Year

(c)


000

20000

40000

60000

80000

100000

120000

140000

160000Pr

emon

soon

1960 1970 1980 1990 2000 2010 20201950Year

(d)


000

10000

20000

30000

40000

50000

60000

Postm

onso

on

1960 1970 1980 1990 2000 2010 20201950Year

(e)




000

20000

40000

60000

80000

100000

120000

140000A

nnua

l

1960 1970 1980 1990 2000 2010 20201950Year

(a)


000

10000

20000

30000

40000

50000

60000

70000

80000

90000

Mon

soon

1960 1970 1980 1990 2000 2010 20201950Year

(b)


000

2000

4000

6000

8000

10000

12000

Win

ter

1960 1970 1980 1990 2000 2010 20201950Year

(c)


000

5000

10000

15000

20000

25000

30000

35000

40000Pr

emon

soon

1960 1970 1980 1990 2000 2010 20201950Year

(d)


000

10000

20000

30000

40000

50000

60000

Postm

onso

on

1960 1970 1980 1990 2000 2010 20201950Year

(e)




4 Discussion




500

700

900

1100

1300

1500

1700

1900

2100

Aver

age a

nnua

l pre

cipi

tatio

n (m

m)

1960 1970 1980 1990 20001950Year




GFDL-ESM2M


Had-GEM2-ES


IPSL-CM5A-LR


MIROC


NoerESM1-M






GFDL-ESM2M


Had-GEM2-ES


IPSL-CM5A-LR


MIROC


NoerESM1-M









0

500

1000

1500

2000

250019

40

1950

1960

1970

1980

1990

2000

2010

Ann

ual

Year

0

500

1000

1500

2000

2500

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

1940

1950

1960

1970

1980

1990

2000

2010

Ann

ual

Year

01020304050607080

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0102030405060708090

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

20

40

60

80

100

120

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

20

40

60

80

100

120

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

1940

1950

1960

1970

1980

1990

2000

2010

Ann

ual

Year

0

500

1000

1500

2000

2500

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0200400600800

100012001400160018002000

Ann

ual

Year

0

500

1000

1500

2000

2500

1940

1950

1960

1970

1980

1990

2000

2010

Ann

ual

MIR

OC

Noe

rESM

1-M

Year

020406080

100120140160180200

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0200400600800

100012001400160018002000

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

Ann

ual

Year

0200400600800

100012001400160018002000

1940

1950

1960

1970

1980

1990

2000

2010

Ann

ual

Year

0

500

1000

1500

2000

2500

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

Ann

ual

Year

Historical

GFD

L-ES

M2M

Had

-GEM

2ES

IPSL

-CM

5A-L

R

































GFDL-ESM2M


Had-GEM2-ES


IPSL-CM5A-LR


MIROC


NoerESM1-M



AnnualMonsoonWinter



0200400600800

1000120014001600

Prec

ipita

tion

(mm

)




5 Conclusion



Data Availability




Acknowledgments


RCP26RCP45

RCP60RCP85

1200

1250

1300

1350

1400

1450

1500

1550

1600

1650

1700

Prec

ipita

tion

(mm

)






References














































































000

50000

100000

150000

200000

250000

300000A

nnua

l

1960 1970 1980 1990 2000 2010 20201950Year

(a)


1960 1970 1980 1990 2000 2010 20201950Year

000

20000

40000

60000

80000

100000

120000

140000

160000

Mon

soon

(b)


000

5000

10000

15000

20000

25000

30000

35000

40000

Win

ter

1960 1970 1980 1990 2000 2010 20201950Year

(c)


000

20000

40000

60000

80000

100000

120000

140000

160000Pr

emon

soon

1960 1970 1980 1990 2000 2010 20201950Year

(d)


000

10000

20000

30000

40000

50000

60000

Postm

onso

on

1960 1970 1980 1990 2000 2010 20201950Year

(e)




000

20000

40000

60000

80000

100000

120000

140000A

nnua

l

1960 1970 1980 1990 2000 2010 20201950Year

(a)


000

10000

20000

30000

40000

50000

60000

70000

80000

90000

Mon

soon

1960 1970 1980 1990 2000 2010 20201950Year

(b)


000

2000

4000

6000

8000

10000

12000

Win

ter

1960 1970 1980 1990 2000 2010 20201950Year

(c)


000

5000

10000

15000

20000

25000

30000

35000

40000Pr

emon

soon

1960 1970 1980 1990 2000 2010 20201950Year

(d)


000

10000

20000

30000

40000

50000

60000

Postm

onso

on

1960 1970 1980 1990 2000 2010 20201950Year

(e)




4 Discussion




500

700

900

1100

1300

1500

1700

1900

2100

Aver

age a

nnua

l pre

cipi

tatio

n (m

m)

1960 1970 1980 1990 20001950Year




GFDL-ESM2M


Had-GEM2-ES


IPSL-CM5A-LR


MIROC


NoerESM1-M






GFDL-ESM2M


Had-GEM2-ES


IPSL-CM5A-LR


MIROC


NoerESM1-M









0

500

1000

1500

2000

250019

40

1950

1960

1970

1980

1990

2000

2010

Ann

ual

Year

0

500

1000

1500

2000

2500

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

1940

1950

1960

1970

1980

1990

2000

2010

Ann

ual

Year

01020304050607080

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0102030405060708090

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

20

40

60

80

100

120

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

20

40

60

80

100

120

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

1940

1950

1960

1970

1980

1990

2000

2010

Ann

ual

Year

0

500

1000

1500

2000

2500

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0200400600800

100012001400160018002000

Ann

ual

Year

0

500

1000

1500

2000

2500

1940

1950

1960

1970

1980

1990

2000

2010

Ann

ual

MIR

OC

Noe

rESM

1-M

Year

020406080

100120140160180200

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0200400600800

100012001400160018002000

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

Ann

ual

Year

0200400600800

100012001400160018002000

1940

1950

1960

1970

1980

1990

2000

2010

Ann

ual

Year

0

500

1000

1500

2000

2500

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

Ann

ual

Year

Historical

GFD

L-ES

M2M

Had

-GEM

2ES

IPSL

-CM

5A-L

R

































GFDL-ESM2M


Had-GEM2-ES


IPSL-CM5A-LR


MIROC


NoerESM1-M



AnnualMonsoonWinter



0200400600800

1000120014001600

Prec

ipita

tion

(mm

)




5 Conclusion



Data Availability




Acknowledgments


RCP26RCP45

RCP60RCP85

1200

1250

1300

1350

1400

1450

1500

1550

1600

1650

1700

Prec

ipita

tion

(mm

)






References




































































000

20000

40000

60000

80000

100000

120000

140000A

nnua

l

1960 1970 1980 1990 2000 2010 20201950Year

(a)


000

10000

20000

30000

40000

50000

60000

70000

80000

90000

Mon

soon

1960 1970 1980 1990 2000 2010 20201950Year

(b)


000

2000

4000

6000

8000

10000

12000

Win

ter

1960 1970 1980 1990 2000 2010 20201950Year

(c)


000

5000

10000

15000

20000

25000

30000

35000

40000Pr

emon

soon

1960 1970 1980 1990 2000 2010 20201950Year

(d)


000

10000

20000

30000

40000

50000

60000

Postm

onso

on

1960 1970 1980 1990 2000 2010 20201950Year

(e)




4 Discussion




500

700

900

1100

1300

1500

1700

1900

2100

Aver

age a

nnua

l pre

cipi

tatio

n (m

m)

1960 1970 1980 1990 20001950Year




GFDL-ESM2M


Had-GEM2-ES


IPSL-CM5A-LR


MIROC


NoerESM1-M






GFDL-ESM2M


Had-GEM2-ES


IPSL-CM5A-LR


MIROC


NoerESM1-M









0

500

1000

1500

2000

250019

40

1950

1960

1970

1980

1990

2000

2010

Ann

ual

Year

0

500

1000

1500

2000

2500

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

1940

1950

1960

1970

1980

1990

2000

2010

Ann

ual

Year

01020304050607080

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0102030405060708090

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

20

40

60

80

100

120

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

20

40

60

80

100

120

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

1940

1950

1960

1970

1980

1990

2000

2010

Ann

ual

Year

0

500

1000

1500

2000

2500

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0200400600800

100012001400160018002000

Ann

ual

Year

0

500

1000

1500

2000

2500

1940

1950

1960

1970

1980

1990

2000

2010

Ann

ual

MIR

OC

Noe

rESM

1-M

Year

020406080

100120140160180200

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0200400600800

100012001400160018002000

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

Ann

ual

Year

0200400600800

100012001400160018002000

1940

1950

1960

1970

1980

1990

2000

2010

Ann

ual

Year

0

500

1000

1500

2000

2500

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

Ann

ual

Year

Historical

GFD

L-ES

M2M

Had

-GEM

2ES

IPSL

-CM

5A-L

R

































GFDL-ESM2M


Had-GEM2-ES


IPSL-CM5A-LR


MIROC


NoerESM1-M



AnnualMonsoonWinter



0200400600800

1000120014001600

Prec

ipita

tion

(mm

)




5 Conclusion



Data Availability




Acknowledgments


RCP26RCP45

RCP60RCP85

1200

1250

1300

1350

1400

1450

1500

1550

1600

1650

1700

Prec

ipita

tion

(mm

)






References




































































4 Discussion




500

700

900

1100

1300

1500

1700

1900

2100

Aver

age a

nnua

l pre

cipi

tatio

n (m

m)

1960 1970 1980 1990 20001950Year




GFDL-ESM2M


Had-GEM2-ES


IPSL-CM5A-LR


MIROC


NoerESM1-M






GFDL-ESM2M


Had-GEM2-ES


IPSL-CM5A-LR


MIROC


NoerESM1-M









0

500

1000

1500

2000

250019

40

1950

1960

1970

1980

1990

2000

2010

Ann

ual

Year

0

500

1000

1500

2000

2500

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

1940

1950

1960

1970

1980

1990

2000

2010

Ann

ual

Year

01020304050607080

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0102030405060708090

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

20

40

60

80

100

120

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

20

40

60

80

100

120

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

1940

1950

1960

1970

1980

1990

2000

2010

Ann

ual

Year

0

500

1000

1500

2000

2500

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0200400600800

100012001400160018002000

Ann

ual

Year

0

500

1000

1500

2000

2500

1940

1950

1960

1970

1980

1990

2000

2010

Ann

ual

MIR

OC

Noe

rESM

1-M

Year

020406080

100120140160180200

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0200400600800

100012001400160018002000

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

Ann

ual

Year

0200400600800

100012001400160018002000

1940

1950

1960

1970

1980

1990

2000

2010

Ann

ual

Year

0

500

1000

1500

2000

2500

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

Ann

ual

Year

Historical

GFD

L-ES

M2M

Had

-GEM

2ES

IPSL

-CM

5A-L

R

































GFDL-ESM2M


Had-GEM2-ES


IPSL-CM5A-LR


MIROC


NoerESM1-M



AnnualMonsoonWinter



0200400600800

1000120014001600

Prec

ipita

tion

(mm

)




5 Conclusion



Data Availability




Acknowledgments


RCP26RCP45

RCP60RCP85

1200

1250

1300

1350

1400

1450

1500

1550

1600

1650

1700

Prec

ipita

tion

(mm

)






References





































































GFDL-ESM2M


Had-GEM2-ES


IPSL-CM5A-LR


MIROC


NoerESM1-M









0

500

1000

1500

2000

250019

40

1950

1960

1970

1980

1990

2000

2010

Ann

ual

Year

0

500

1000

1500

2000

2500

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

1940

1950

1960

1970

1980

1990

2000

2010

Ann

ual

Year

01020304050607080

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0102030405060708090

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

20

40

60

80

100

120

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

20

40

60

80

100

120

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

1940

1950

1960

1970

1980

1990

2000

2010

Ann

ual

Year

0

500

1000

1500

2000

2500

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0200400600800

100012001400160018002000

Ann

ual

Year

0

500

1000

1500

2000

2500

1940

1950

1960

1970

1980

1990

2000

2010

Ann

ual

MIR

OC

Noe

rESM

1-M

Year

020406080

100120140160180200

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0200400600800

100012001400160018002000

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

Ann

ual

Year

0200400600800

100012001400160018002000

1940

1950

1960

1970

1980

1990

2000

2010

Ann

ual

Year

0

500

1000

1500

2000

2500

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

Ann

ual

Year

Historical

GFD

L-ES

M2M

Had

-GEM

2ES

IPSL

-CM

5A-L

R

































GFDL-ESM2M


Had-GEM2-ES


IPSL-CM5A-LR


MIROC


NoerESM1-M



AnnualMonsoonWinter



0200400600800

1000120014001600

Prec

ipita

tion

(mm

)




5 Conclusion



Data Availability




Acknowledgments


RCP26RCP45

RCP60RCP85

1200

1250

1300

1350

1400

1450

1500

1550

1600

1650

1700

Prec

ipita

tion

(mm

)






References





































































0

500

1000

1500

2000

250019

40

1950

1960

1970

1980

1990

2000

2010

Ann

ual

Year

0

500

1000

1500

2000

2500

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

1940

1950

1960

1970

1980

1990

2000

2010

Ann

ual

Year

01020304050607080

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0102030405060708090

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

20

40

60

80

100

120

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

20

40

60

80

100

120

2010

2020

2030

2040

2050

2060

2070

2080

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2100

2110

2010

2020

2030

2040

2050

2060

2070

2080

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2100

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2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

1940

1950

1960

1970

1980

1990

2000

2010

Ann

ual

Year

0

500

1000

1500

2000

2500

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0200400600800

100012001400160018002000

Ann

ual

Year

0

500

1000

1500

2000

2500

1940

1950

1960

1970

1980

1990

2000

2010

Ann

ual

MIR

OC

Noe

rESM

1-M

Year

020406080

100120140160180200

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0200400600800

100012001400160018002000

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

Ann

ual

Year

0200400600800

100012001400160018002000

1940

1950

1960

1970

1980

1990

2000

2010

Ann

ual

Year

0

500

1000

1500

2000

2500

2000

2020

2040

2060

2080

2100

2120

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

2110

Ann

ual

Year

0

500

1000

1500

2000

2500

Ann

ual

Year

Historical

GFD

L-ES

M2M

Had

-GEM

2ES

IPSL

-CM

5A-L

R

































GFDL-ESM2M


Had-GEM2-ES


IPSL-CM5A-LR


MIROC


NoerESM1-M



AnnualMonsoonWinter



0200400600800

1000120014001600

Prec

ipita

tion

(mm

)




5 Conclusion



Data Availability




Acknowledgments


RCP26RCP45

RCP60RCP85

1200

1250

1300

1350

1400

1450

1500

1550

1600

1650

1700

Prec

ipita

tion

(mm

)






References







































































GFDL-ESM2M


Had-GEM2-ES


IPSL-CM5A-LR


MIROC


NoerESM1-M



AnnualMonsoonWinter



0200400600800

1000120014001600

Prec

ipita

tion

(mm

)




5 Conclusion



Data Availability




Acknowledgments


RCP26RCP45

RCP60RCP85

1200

1250

1300

1350

1400

1450

1500

1550

1600

1650

1700

Prec

ipita

tion

(mm

)






References




































































5 Conclusion



Data Availability




Acknowledgments


RCP26RCP45

RCP60RCP85

1200

1250

1300

1350

1400

1450

1500

1550

1600

1650

1700

Prec

ipita

tion

(mm

)






References



































































References



































































long-termrainfalltrendsandfutureprojectionsoverxijiang ......2019/10/09 · mann–kendall test...

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