%0 Journal Article
%9 ACL : Articles dans des revues avec comité de lecture répertoriées par l'AERES
%A Abbas, A.
%A Yang, Y.
%A Pan, M.
%A Tramblay, Yves
%A Shen, C. P.
%A Ji, H. Y.
%A Gebrechorkos, S.
%A Pappenberger, F.
%A Pyo, J.
%A Feng, D. P.
%A Huffman, G.
%A Nguyen, P.
%A Massari, C.
%A Brocca, L.
%A Tan, J.
%A Beck, H.
%T Comprehensive global assessment of 24 gridded precipitation datasets across 18 428 catchments using hydrological modeling
%D 2026
%L fdi:010097405
%G ENG
%J Hydrology and Earth System Sciences
%@ 1027-5606
%M ISI:001783148600001
%N 11
%P 3399-3423
%R 10.5194/hess-30-3399-2026
%U https://www.documentation.ird.fr/hor/fdi:010097405
%> https://horizon.documentation.ird.fr/exl-doc/pleins_textes/2026-07/010097405.pdf
%V 30
%W Horizon (IRD)
%X 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 difficult for users. We conducted the most comprehensive global evaluation to date of gridded (sub-)daily P datasets using hydrological modeling. A total of 24 datasets - derived from satellite, (re)analysis, gauge sources, or combinations thereof - were assessed. To evaluate their performance, we calibrated the conceptual hydrological model HBV against observed daily streamflow for 18 428 catchments (each <10000km(2)) worldwide, using each P dataset as input. The Kling-Gupta Efficiency (KGE) was used as performance metric, with the calibration score serving as proxy for P dataset performance. Overall, Multi-Source Weighted-Ensemble Precipitation (MSWEP) V2.8 demonstrated the best performance (median KGE of 0.78), 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 Global Precipitation Mission plus Soil Moisture to RAIN (GPM + SM2RAIN) dataset performed best (median KGE of 0.64). The Global Data Assimilation System (GDAS) analysis ranked highest among the (re)analyses (median KGE of 0.72), slightly outperforming the widely used European Centre for Medium-range Weather Forecasts ReAnalysis 5 (ERA5; median KGE of 0.71). Performance varied across K & ouml;ppen-Geiger climate zones, with the highest scores in polar (E) regions (median KGE of 0.76 across datasets) and the lowest in arid (B) regions (median KGE of 0.53 across datasets). Spatial correlation analysis between catchment attributes and KGE scores identified aridity index, potential evaporation, and P occurrence as the strongest predictors of performance. Our assessment revealed significant regional differences in dataset performance and error characteristics, emphasizing the importance of careful dataset selection for water resource management, hazard assessment, agricultural planning, and environmental monitoring.
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