Highlights What are the main findings? MSWEP v2.8 performed at par with IMERG and CMORPH over the UAE (2004-2020) with lower RMSE and balanced KGE values. Seasonal and event-based analyses show MSWEP reliably captures both frontal and convective rainfall events in arid environments. What are the implications of the main findings? MSWEP provides dependable rainfall estimates for flood forecasting, drought monitoring, and water resource modeling in data-scarce regions. The results support MSWEP as a benchmark product for regional calibration and climate impact assessments across the Arabian Peninsula.Highlights What are the main findings? MSWEP v2.8 performed at par with IMERG and CMORPH over the UAE (2004-2020) with lower RMSE and balanced KGE values. Seasonal and event-based analyses show MSWEP reliably captures both frontal and convective rainfall events in arid environments. What are the implications of the main findings? MSWEP provides dependable rainfall estimates for flood forecasting, drought monitoring, and water resource modeling in data-scarce regions. The results support MSWEP as a benchmark product for regional calibration and climate impact assessments across the Arabian Peninsula.Abstract Accurate precipitation data is vital for hydrological modeling, climate research, and water resource management, especially in arid regions like the United Arab Emirates (UAE), where rainfall is sparse and highly variable. This study assesses the performance of the Multi-Source Weighted-Ensemble Precipitation v2.8 (MSWEP) dataset against ground-based gauge data and three satellite precipitation products-CMORPH, IMERG, and GSMaP-across the UAE from 2004 to 2020. Evaluation metrics include statistical, categorical, and extreme precipitation indices. MSWEP shows a moderate correlation with gauge data (mean CC = 0.62), performing better than CMORPH (0.54) but below IMERG (0.68). It also yields lower RMSE and MAE than CMORPH and GSMaP, indicating improved error metrics. However, MSWEP overestimates light rainfall and underestimates extreme events, reflected in a lower KGE (0.42) and weak performance in the 95th percentile rainfall, especially in coastal and mountainous areas. Seasonal analysis reveals overestimation in winter and underestimation during summer convective storms. While MSWEP offers strong global coverage and temporal consistency, its application in arid environments like the UAE requires bias correction. These findings highlight the need for integrating multiple datasets and regional adjustments to enhance rainfall estimation accuracy for hydrological and climate-related applications.