@article{fdi:010097405,
  title = {{C}omprehensive global assessment of 24 gridded precipitation datasets across 18 428 catchments using hydrological modeling},
  author = {{A}bbas, {A}. and {Y}ang, {Y}. and {P}an, {M}. and {T}ramblay, {Y}ves and {S}hen, {C}. {P}. and {J}i, {H}. {Y}. and {G}ebrechorkos, {S}. and {P}appenberger, {F}. and {P}yo, {J}. and {F}eng, {D}. {P}. and {H}uffman, {G}. and {N}guyen, {P}. and {M}assari, {C}. and {B}rocca, {L}. and {T}an, {J}. and {B}eck, {H}.},
  editor = {},
  language = {{ENG}},
  abstract = {{N}umerous gridded precipitation ({P}) datasets have been developed to address a variety of needs and challenges. {H}owever, selecting the most suitable and reliable dataset remains difficult for users. {W}e 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. {T}o 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. {T}he {K}ling-{G}upta {E}fficiency ({KGE}) was used as performance metric, with the calibration score serving as proxy for {P} dataset performance. {O}verall, {M}ulti-{S}ource {W}eighted-{E}nsemble {P}recipitation ({MSWEP}) {V}2.8 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. {A}mong the purely satellite-based {P} datasets, the soil moisture- and microwave-based {G}lobal {P}recipitation {M}ission plus {S}oil {M}oisture to {RAIN} ({GPM} + {SM}2{RAIN}) dataset performed best (median {KGE} of 0.64). {T}he {G}lobal {D}ata {A}ssimilation {S}ystem ({GDAS}) analysis ranked highest among the (re)analyses (median {KGE} of 0.72), slightly outperforming the widely used {E}uropean {C}entre for {M}edium-range {W}eather {F}orecasts {R}e{A}nalysis 5 ({ERA}5; median {KGE} of 0.71). {P}erformance varied across {K} & ouml;ppen-{G}eiger 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). {S}patial correlation analysis between catchment attributes and {KGE} scores identified aridity index, potential evaporation, and {P} occurrence as the strongest predictors of performance. {O}ur 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.},
  keywords = {},
  booktitle = {},
  journal = {{H}ydrology and {E}arth {S}ystem {S}ciences},
  volume = {30},
  numero = {11},
  pages = {3399--3423},
  ISSN = {1027-5606},
  year = {2026},
  DOI = {10.5194/hess-30-3399-2026},
  URL = {https://www.documentation.ird.fr/hor/fdi:010097405},
}