Evaluating the performance of eight global gridded precipitation datasets across Iran

Precipitation is one of the crucial variables in hydrological and water resources studies. Runoff, soil moisture, groundwater recharge, other change with amount precipitation a region. Thus, accurate estimation major issue field modeling. Most studies management decisions Iran are established using situ observation precipitation. However, due to limited spatial distribution lack long-term high-quality data many stations, this information usually does not fully meet requirements given study. In study, accuracies eight global gridded datasets including Global Climatology Centre (GPCC), Climatic Research Unit (CRU), Climate Prediction Center (CPC), Hazards Group InfraRed Station (CHIRPS), PERSIANN-Climate Data Record (PERSIANN-CDR), Project (GPCP), Modern-Era Retrospective Analysis for Applications version 2 (MERRA2), ECMWF Reanalysis 5 (ERA5) were evaluated identify strengths weaknesses each dataset different regions main catchments Iran. The 96 synoptic stations during years 1987–2016 was used as basis evaluations. evaluations made at monthly, seasonal, annual scales grid-based evaluation areal average scale. addition precipitation, three statistical performance indices (i.e., correlation coefficient, RMSE, relative bias) metrics such probability similarity (using Anderson-darling test), empirical cumulative function (ECDF), Taylor diagrams. results indicated that varied by catchment. Except central parts, GPCC best 44% grids terms time series pattern recognition. It showed coefficients 0.71, 0.80, 0.85, scales, respectively. areas, MERRA2 estimated catchment + mm difference, CRU had coefficient greater than 0.90. Moreover, CHIRPS bias south southwestern catchments, so negligible these most months. This study provides rankings recommendations selecting an appropriate alternative dataset, which, turn, knowledge required monitoring systems modeling need high-resolution input data.