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Saudi Rainfall (SaRa): hourly 0.1° gridded rainfall (1979–present) for Saudi Arabia via machine learning fusion of satellite and model data

Lookup NU author(s): Dr Amy GreenORCiD

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

Abstract

© Author(s) 2025.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 1979 to the present and is continuously updated with a latency of less than 2 h. 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 (randomly 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-day 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 https://www. gloh2o.org/sara (last access: 24 September 2025) – 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.

Publication metadata

Author(s): Wang X, Alharbi RS, Baez-Villanueva OM, Green A, McCabe MF, Wada Y, Van Dijk AIJM, Abid MA, Beck HE

Publication type: Article

Publication status: Published

Journal: Hydrology and Earth System Sciences

Year: 2025

Volume: 29

Issue: 19

Pages: 4983-5003

Online publication date: 08/10/2025

Acceptance date: 14/07/2025

Date deposited: 27/10/2025

ISSN (print): 1027-5606

ISSN (electronic): 1607-7938

Publisher: Copernicus Publications

URL: https://doi.org/10.5194/hess-29-4983-2025

DOI: 10.5194/hess-29-4983-2025

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