Comprehensive Global Assessment of 24 Gridded Precipitation Datasets Across 18 428 Catchments Using Hydrological Modeling
Year: 2026
Type: article
Abstract: . 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) world- wide, 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.
Institutions: King Abdullah University of Science and Technology, Scripps Institution of Oceanography, University of California San Diego, Université de Montpellier, Institut de Recherche pour le Développement, Pennsylvania State University, University of Oxford, European Centre for Medium-Range Weather Forecasts, Pusan National University, Stanford University, Goddard Space Flight Center, University of California, Irvine, Center for Hydrometeorology and Remote Sensing, Research Institute for Geo-Hydrological Protection, National Research Council
Language: English
Subfield: Atmospheric Science
Field: Earth and Planetary Sciences
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,725
OpenAlex ID: W7163368214