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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
03 Jun 2026
Research article |
| 03 Jun 2026
| 03 Jun 2026
Comprehensive Global Assessment of 24 Gridded Precipitation Datasets Across 18 428 Catchments Using Hydrological Modeling
Ather Abbas
Division of Physical Science and Engineering, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia
Yuan Yang
Center for Western Weather and Water Extremes, Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA
Center for Western Weather and Water Extremes, Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA
Yves Tramblay
Espace Dev (University Montpellier, IRD), Montpellier, France
Chaopeng Shen
Civil and Environmental Engineering, The Pennsylvania State University, PA, USA
Haoyu Ji
Civil and Environmental Engineering, The Pennsylvania State University, PA, USA
Solomon H. Gebrechorkos
School of Geography and the Environment, University of Oxford, Oxford, UK
Florian Pappenberger
European Centre for Medium-range Weather Forecasts, Reading, UK
JongCheol Pyo
Department of Environmental Engineering, Pusan National University, Busan, 46241, Republic of Korea
Dapeng Feng
Department of Earth System Science, Stanford University, Stanford, CA 94305, USA
George Huffman
NASA Goddard Space Flight Center, Greenbelt, MD, USA
Phu Nguyen
Center for Hydrometeorology and Remote Sensing (CHRS), Department of Civil and Environmental Engineering, University of California, Irvine, CA 92697, USA
Christian Massari
Research Institute for Geo-Hydrological Protection (CNR-IRPI), National Research Council, Perugia, Italy
Luca Brocca
Research Institute for Geo-Hydrological Protection (CNR-IRPI), National Research Council, Perugia, Italy
Jackson Tan
NASA Goddard Space Flight Center, Greenbelt, MD, USA
Hylke E. Beck
CORRESPONDING AUTHOR
Division of Physical Science and Engineering, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia
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Cited
4 citations as recorded by crossref.
- Distinct hydrologic response patterns and trends worldwide revealed by physics-embedded learning H. Ji et al. https://doi.org/10.1038/s41467-025-64367-1
- Uncertainty propagation from gridded precipitation datasets to streamflow simulations: application to the Reno River basin (Italy) O. Cenobio-Cruz & G. Di Baldassarre https://doi.org/10.1080/02626667.2025.2590597
- Predicting aflatoxin risk with seasonal meteorological forecast D. Balková et al. https://doi.org/10.1088/1748-9326/ae3629
- Drought Monitoring to Build Climate Resilience in Pacific Island Countries S. Marcus et al. https://doi.org/10.3390/cli13090172
4 citations as recorded by crossref.
- Distinct hydrologic response patterns and trends worldwide revealed by physics-embedded learning H. Ji et al. https://doi.org/10.1038/s41467-025-64367-1
- Uncertainty propagation from gridded precipitation datasets to streamflow simulations: application to the Reno River basin (Italy) O. Cenobio-Cruz & G. Di Baldassarre https://doi.org/10.1080/02626667.2025.2590597
- Predicting aflatoxin risk with seasonal meteorological forecast D. Balková et al. https://doi.org/10.1088/1748-9326/ae3629
- Drought Monitoring to Build Climate Resilience in Pacific Island Countries S. Marcus et al. https://doi.org/10.3390/cli13090172
Latest update: 22 Jun 2026
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.
Our study evaluated 24 precipitation datasets using a hydrological model at global scale to...
