No abstract

Global Climate Models (GCMs) are considered the most feasible tools to estimate future climate change.The objective of this study was to assess the interpretation of 19 GCMs of Coupled Model Intercomparison Project 6 (CMIP6) in replicating the historical precipitation and temperature of climate prediction center data for the Amu Darya river basin (ADRB) and the projection of climate of the basin using the selected GCMs. The Kling Gupta efficiency (KGE) metric was used to assess the effectiveness of GCMs to simulate the annual geographic variability of precipitation, maximum and minimum temperature (Pr, Tmx and Tmn). A multi-criteria decision-making approach (MCDMA) was used to integrate the KGE values to rank GCMs. The results revealed that MPI-ESM1-2-LR, CMCC-ESM2, INM-CM4-8 and AWI-CM-1-1-MR are the best in replicating observed Pr, Tmx and Tmn in ADRB. Projection of climate employing the selected GCMs indicated an increase in precipitation (9.9-12.4%) and temperature (1.3-5.5 C) in the basin for all the shared socioeconomic pathways (SSPs), particularly for the far future . A signi cant variation can be seen in temperature over the different climatic zone. However, the intercomparison of selected GCM projected revealed high uncertainty in the projected climate. The uncertainty is higher in the far future and higher SSPs compared to the near future and lower SSPs.

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