Decade-Long Cloud-Resolving Model Simulations for Arabian Peninsula Winter Precipitation: Climatology and Extremes

Abstract The spatiotemporal distribution of winter precipitation over the Arabian Peninsula (AP) is crucial for managing various socioeconomic sectors. Due to limited observational data, high-resolution atmospheric models are often used to investigate rainfall distribution across the region. However, uncertainties in current models are strongly influenced by the representation of clouds, moist convection, and complex topography. Reducing the grid spacing to a few kilometers using a cloud-resolving model (CRM) allows for improved treatment of clouds and associated hydro-climatological processes, which can help reduce uncertainties in model precipitation physics. To address this, we conducted multiyear CRM simulations using the Weather Research and Forecasting (WRF) Model at 2-km horizontal resolution, specifically targeting the winter season, to simulate seasonal precipitation patterns over the AP from 2006 to 2016. These CRM outputs are validated against available in situ, gridded, remotely sensed observations, and reanalyses fields. Furthermore, we used ERA5 reanalysis fields to evaluate circulation, thermodynamic, and microphysical processes in the CRM. Our results demonstrate that CRM-simulated rainfall agrees well with the observed rainfall patterns, albeit for some wet bias (ranging between 0.83 and 1.88) and root-mean-square error (0.81–1.40) over mountainous regions. Precipitation statistics confirm that the CRM adequately captures the spatial extent and variability of winter rainfall over the AP. The model also reveals that moisture transport from the Red Sea, Mediterranean Sea, and Arabian Sea substantially influences the regional precipitation patterns. Notably, the CRM enhances the simulation of extreme precipitation events, accurately capturing their spatial distribution, intensity, and frequency. We also examined the underlying physical mechanisms driving these precipitation dynamics using the CRM simulations. Overall, the findings demonstrate that the CRM provides a more realistic representation of fine-scale precipitation features and the associated physical processes across the AP. This study offers valuable insights into the application of high-resolution CRM frameworks for improving the prediction of precipitation extremes in this arid region. Significance Statement Understanding the spatiotemporal distribution of winter precipitation over the Arabian Peninsula (AP) is essential for managing water resources and mitigating climate-related risks. Due to sparse observational data and the region’s complex terrain, accurately simulating rainfall remains a challenge. This study leverages high-resolution, cloud-resolving simulations using the Weather Research and Forecasting (WRF) Model at 2-km resolution to improve, for the first time, the representation of precipitation processes and associated dynamics over the region. Our results show that the model closely aligns with multiple observational datasets and successfully captures key features of winter rainfall, including extreme events and moisture transport pathways. These findings highlight the value of high-resolution modeling in reducing uncertainties and advancing predictive capabilities for precipitation extremes in arid environments like the AP.