Added Value of CESM ‐Driven WRF for Mean and Extreme Precipitation Over Complex Terrain in Southwest Asia

Abstract

Southwest Asia (SWA) is a climatically sensitive region where water resources are determined by the complex interactions between the Indian Summer Monsoon and Mediterranean winter systems. Coarse‐resolution global climate models (GCMs) have difficulties in capturing the arid‐to‐semi‐arid hydroclimate of the region, which is characterized by high variability and orographically intensified precipitation. This study evaluates the extent to which dynamical downscaling improves the representation of mean and extreme precipitation in SWA. We use the Weather Research and Forecasting (WRF) model at a resolution of 10 km, driven by boundary conditions from the Community Earth System Model (CESM v1.2.2). We compare the atmospheric component of the native CESM (~2° resolution), the downscaled WRF simulation, and ERA5 for the common period 1950–2002, with supplementary validation against two satellite‐gauge blended products, CHIRPS v3.0 and PERSIANN‐CDR v01r01, for 1983–2002. Our analysis reveals three main findings. First, WRF significantly improves the annual cycle of precipitation, closely agreeing with ERA5. Meanwhile CESM has an inverted annual cycle relative to ERA5, substantially underestimating winter precipitation, while showing a summer wet bias. This summer overestimation is also identified in the extreme precipitation of CESM in the southern part of SWA. Second, the spatial patterns of seasonal mean and extreme precipitation generally agree between WRF and ERA5, with WRF correctly identifying the primary precipitation maxima across all seasons, consistent with the locations shown by the satellite products, though WRF consistently overestimates the magnitude and spatial extent of these maxima relative to ERA5. Third, in areas of complex orography, such as the Zagros Mountains, WRF exhibits precipitation values higher than ERA5. This is attributed to the higher‐resolution topographic representation of WRF, which enhances orographic uplift, and its explicit treatment of convection. These findings demonstrate that dynamical downscaling substantially reduces large‐scale GCM biases and provides a more consistent representation of precipitation in a region of complex orography and limited observational data availability.