Saudi Rainfall (SaRa): Hourly 0.1° Gridded Rainfall (1979–Present) for Saudi Arabia via Machine Learning Fusion of Satellite and Model Data

Abstract. We introduce Saudi Rainfall (SaRa), a gridded historical and near real-time precipitation (P) product specifically designed for the Arabian Peninsula, one of the most arid, water-stressed, and data-sparse regions on Earth. The product has an hourly 0.1° resolution spanning from 1979 to the present and is continuously updated with a latency of less than two hours. The algorithm underpinning the product involves 18 machine learning model stacks trained for different combinations of satellite and (re)analysis P products along with several static predictors. As a training target, hourly and daily P observations from gauges in Saudi Arabia (n=113) and globally (n=14,256) are used. To evaluate the performance of SaRa, we carried out the most comprehensive evaluation of gridded P products in the region to date, using observations from independent gauges (excluded from training) in Saudi Arabia as a reference (n=119). Among the 20 evaluated P products, our new product, SaRa, consistently ranked first across all evaluation metrics, including the Kling-Gupta Efficiency (KGE), correlation, bias, peak bias, wet days bias, and critical success index. Notably, SaRa achieved a median KGE — a summary statistic combining correlation, bias, and variability — of 0.36, while widely used non-gauge-based products such as CHIRP, ERA5, GSMaP V8, and IMERG-L V07 achieved values of -0.07, 0.21, -0.13, and -0.39, respectively. SaRa also outperformed four gauge-based products such as CHIRPS V2, CPC Unified, IMERG-F V07, and MSWEP V2.8 which had median KGE values of 0.17, -0.03, 0.29, and 0.20, respectively. Our new P product — available at www.gloh2o.org/sara — addresses a crucial need in the Arabian Peninsula, providing a robust and reliable dataset to support hydrological modeling, water resource assessments, flood management, and climate research.