Future Climate Projections Using SDSM and LARS-WG Downscaling Methods for CMIP5 GCMs over the Transboundary Jhelum River Basin of the Himalayas Region

Climate change is one of the leading issues affecting river basins due to its direct impacts on cryosphere and hydrosphere. General circulation models (GCMs) are widely applied tools assess climate but coarse spatial resolution GCMs limit their application for local studies. This study selected five CMIP5 (CCSM4, HadCM3, GFDL-CM3, MRI-CGCM3 CanESM2) performance evaluation ranked by Nash–Sutcliffe coefficient (NSE) Kling–Gupta Efficiency (KGE). CCSM4 HadCM3 large-scale predictors were favored based ranks (0.71 0.68, respectively) statistical downscaling techniques downscale climatic indicators Tmax, Tmin precipitation. The two techniques, Statistical Downscaling Methods (SDSM) Long Ashton Research Station Weather Generator (LARS-WG), examined using Mean Absolute Error (MAE), Root Square (RMSE), bias, NSE KGE with weights (Wi) validation period. results measures proved SDSM more efficient (0.67) in comparison LARS-WG (0.51) time Jhelum River basin. findings revealed that simulation Tmax comparable reference data period except extreme events 21st century projections exhibited a significant rise (2.37–4.66 °C), (2.47–4.52 °C) precipitation (7.4–11.54%) RCP-4.5 RCP-8.5, respectively. Overall, depicted winter pre-monsoon seasons potentially most affected terms warming precipitation, which has potential alter runoff