Comprehensive Global Assessment of 24 Gridded Precipitation Datasets Across 18&thinsp;428 Catchments Using Hydrological Modeling

Abbas, Ather; Yang, Yuan; Pan, Ming; Tramblay, Yves; Shen, Chaopeng; Ji, Haoyu; Gebrechorkos, Solomon H.; Pappenberger, Florian; Pyo, JongCheol; Feng, Dapeng; Huffman, George; Nguyen, Phu; Massari, Christian; Brocca, Luca; Tan, Jackson; Beck, Hylke E.

doi:10.5194/hess-30-3399-2026

Articles | Volume 30, issue 11

https://doi.org/10.5194/hess-30-3399-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-30-3399-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 30, issue 11

Research article

|

03 Jun 2026

Research article |

| 03 Jun 2026

Comprehensive Global Assessment of 24 Gridded Precipitation Datasets Across 18 428 Catchments Using Hydrological Modeling

Ather Abbas, Yuan Yang, Ming Pan, Yves Tramblay, Chaopeng Shen, Haoyu Ji, Solomon H. Gebrechorkos, Florian Pappenberger, JongCheol Pyo, Dapeng Feng, George Huffman, Phu Nguyen, Christian Massari, Luca Brocca, Jackson Tan, and Hylke E. Beck

Supplement

https://doi.org/10.5194/hess-30-3399-2026-supplement

Data sets

SM2RAIN-ASCAT (2007-2022): global daily satellite rainfall from ASCAT soil moisture (2.1.2n) L. Brocca et al. https://doi.org/10.5281/zenodo.10376109

SM2RAIN-CCI (1 Jan 1998 – 31 December 2015) global daily rainfall dataset (Version 2) L. Ciabatta et al. https://doi.org/10.5281/zenodo.1305021

GPM+SM2RAIN (2007-2018): quasi-global 25km/daily rainfall product from the integration of GPM and SM2RAIN-based rainfall products (0.1.0) C. Massari https://doi.org/10.5281/zenodo.3854817

ERA5 hourly data on single levels from 1940 to present Copernicus Climate Change Service, Climate Data Store https://doi.org/10.24381/cds.adbb2d47

Model code and software

rain2flow AtrCheema https://github.com/AtrCheema/rain2flow

Short summary

Our study evaluated 24 precipitation datasets using a hydrological model at global scale to assess their suitability and accuracy. We found that MSWEP (Multi-Source Weighted-Ensemble Precipitation) V2.8 excels due to its ability to integrate data from multiple sources, while others, such as IMERG (Integrated Multi-satellitE Retrievals for Global Precipitation Mission) and GDAS (Global Data Assimilation System), demonstrated strong regional performances. This research assists in selecting the appropriate dataset for applications in water resource management, hazard assessment, agriculture, and environmental monitoring.