Ranking of gridded precipitation datasets by merging compromise programming and global performance index: a case study of the Amu Darya basin

Accurate representation of precipitation over time and space is vital for hydro-climatic studies. Appropriate selection gridded data (GPD) important regions where long-term in situ records are unavailable gauging stations sparse. This study was an attempt to identify the best GPD data-poor Amu Darya River basin, a major source freshwater Central Asia. The performance seven GPDs 55 gauge locations assessed. A novel algorithm, based on integration compromise programming index (CPI) global (GPI) as part multi-criteria group decision-making (MCGDM) method, employed evaluate GPDs. CPI GPI were estimated using six statistical indices representing degree similarity between properties. results indicated great variability inconsistency different ranked Climate Prediction Center (CPC) product 20 out analysed, followed by Princeton University Global Meteorological Forcing (PGF) Hazards Group Infrared Precipitation with Station (CHIRPS). Conversely, CPC 25 stations, PGF CHRIPS. Integration ranking through MCGDM revealed that basin. also closely aligned CPC.