- 1King Abdullah University of Science and Technology, Physical Sciences and Engineering Division, Physical Sciences and Engineering Division, Ulsan, Saudi Arabia (ather_abbas786@yahoo.com)
- 2Center for Western Weather and Water Extremes, Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA
- 3Espace Dev (University Montpellier, IRD), Montpellier, France
- 4Civil and Environmental Engineering, The Pennsylvania State University, PA, USA
- 5School of Geography and the Environment, University of Oxford, Oxford, UK
- 6European Centre for Medium-range Weather Forecasts, Reading/Bonn/Bologna, UK/DE/IT
- 7Department of Environmental Engineering, Pusan National University, Busan, 46241, Republic of Korea
- 8Department of Earth System Science, Stanford University, Stanford, CA 94305, USA
- 9NASA Goddard Space Flight Center, Greenbelt, MD, USA
- 10Center for Hydrometeorology and Remote Sensing (CHRS), Department of Civil and Environmental Engineering, University of California, Irvine, CA 92697, USA
- 11Research Institute for Geo-Hydrological Protection (CNR-IRPI), National Research Council, Perugia, Italy
Numerous gridded precipitation (P) datasets have been developed to address a variety of needs and challenges. However, selecting the most suitable and reliable dataset remains a challenge for users. We conducted the most comprehensive global evaluation to date of gridded (sub-)daily $P$ datasets using hydrological modeling. A total of 23 datasets, derived from satellite, model, gauge sources, or their combinations thereof, were assessed. To evaluate their performance, we calibrated the conceptual hydrological model HBV against observed daily streamflow for 16,295 catchments (each <10,000~km2) worldwide, using each P dataset as input. The Kling-Gupta Efficiency (KGE) was used as the performance metric and the calibration score served as a proxy for P dataset performance. Overall, MSWEP V2.8 demonstrated the highest performance (median KGE of 0.75), highlighting the value of merging P estimates from diverse data sources and applying daily gauge corrections. Among the purely satellite-based P datasets, the soil moisture- and microwave-based GPM+SM2RAIN dataset performed best (median KGE of 0.60), while the JRA-3Q reanalysis ranked highest among the purely model-based datasets (median KGE of 0.67), outperforming the widely used ERA5 reanalysis (median KGE of 0.59). Performance varied across Köppen-Geiger climate zones, with the best results in polar (E) regions (median KGE of 0.74 across datasets) and the lowest in arid (B) regions (median KGE of 0.33 across datasets). We further examined the spatial relationships between catchment attributes and KGE scores, identifying potential evaporation, air temperature, solid P fraction, and latitude as the strongest predictors of performance. Our analysis revealed significant regional differences in dataset performance and heterogeneity in P error characteristics, underscoring the critical importance of careful dataset selection for water resource management, hazard assessment, agricultural planning, and environmental monitoring.
How to cite: Abbas, A., Yang, Y., Pan, M., Tramblay, Y., Shen, C., Ji, H., Gebrechorkos, S. H., Pappenberger, F., Pyo, J., Feng, D., Huffman, G., Nguyen, P., Massari, C., Brocca, L., Jackson, T., and Beck, H. E.: Comprehensive Global Assessment of 23 Gridded PrecipitationDatasets Across 16,295 Catchments Using Hydrological Modeling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19338, https://doi.org/10.5194/egusphere-egu25-19338, 2025.
