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Precipitation is a crucial influencing factor in the global hydrological cycle and terrestrial ecosystems. Previous studies have mostly focused on the historical precipitation variations over the Tibetan Plateau (TP). However, little attention has been given to the differences between convective and stratiform precipitation under future climate change scenarios. In order to acquire the high‐resolution precipitation information over the TP, the dynamical downscaling simulation was conducted utilizing a regional climate model (Weather Research and Forecasting [WRF] model) forced with a global climate model (Community Climate System Model [CCSM]). In this study, we focus on projections of future changes in convective and stratiform precipitation over the TP. The downscaling results during a 26‐year period 1980–2005 are compared for dry season, wet season and annual mean precipitation against the gridded observation dataset. Then, the changes in the wet season convective and stratiform precipitation with warming between CCSM and WRF‐CCSM were compared in the future period 2070–2099 under two scenarios (RCP4.5 and RCP8.5). Compared with the coarse‐resolution forcing, the historical precipitation of dry season, wet season and annual mean over the TP were found to better reproduce in WRF‐CCSM simulation. WRF‐CCSM projects an opposite spatial pattern of total precipitation change between the northern and southern TP in the future under different scenarios, which were attributed to the decreased stratiform precipitation in the southern TP and the increased convective precipitation in the northern TP, contrasting with the uniform increase of total precipitation in its forcing. The surface air temperature projected in WRF‐CCSM is larger increase than in the CCSM forcing. Compared to the CCSM forcing, WRF‐CCSM projects more pronounced increase in convective precipitation in response to rapid warming, and increasingly dominates changes of total precipitation with temperature exceeds the Clausius–Clapeyron rate over the northern TP. [ABSTRACT FROM AUTHOR]

ABSTRACT Quality of a downscaling depends primarily on the quality of the driving global climate model ( GCM). In this study, historical atmospheric conditions simulated by 14 GCMs in CMIP5 are evaluated for downscaling applications centred over the Tibetan Plateau ( TP) with ERA-Interim reanalysis as reference. Another reanalysis NCEP-DOE is also used to estimate the uncertainty associated with the reanalyses. Performances of six frequently used GCM variables, involving atmospheric circulation, air temperature and humidity, are evaluated in terms of biases, spatial correlation coefficient, mean absolute error as well as distinct seasonal features. To detect distributional biases, the two-sample Kolmogorov-Smirnov test ( KS test) is applied to both the original time series and their anomalies on the monthly scale. A spatial ranking scheme is finally applied to objectively quantify overall relative merits of the GCMs over this region. We found that differences between two reanalysis datasets are negligible over this region. Regarding the GCMs' performances, the biases of the simulated variables show remarkable differences among models. Sea level pressure and 500 hPa geopotential height are well simulated by all the GCMs, whereas specific humidity at 600 hPa has a significant dry bias and temperature at 500 hPa has a sizable cold bias. The spatial pattern of the upper-tropospheric circulation is relatively poorly simulated. The KS test suggests that the climatic mean and higher order moments play about an equal role in causing the errors. According to the ranking scores, CCSM4, CNRM-CM5, MPI-ESM-LR, NorESM1-M, MIROC4h, MPI_ESM_MR and CSIRO-MK are relatively superior to other GCMs for this region. [ABSTRACT FROM AUTHOR]