Integrating double techniques of statistical downscaling and bias correction to reduce bias in projections trends of future climate datasets.

Climate change is one of the most significant challenges of the 21st century, particularly its impact on surface water availability in arid and hyper-arid regions within the Euphrates River Basin. This study aims to analyze the impacts of climate change using five global climate models (GCMs) within the Coupled Model Intercomparison Project Phase 6 (CMIP6, IPCC 2021). Model outputs were statistically downscaled using Statistical Downscaling Model (SDSM 6.1) under three greenhouse gas emission scenarios, and bias correction was performed using Climate Model Data for Hydrologic Modeling (CMhyd 1.02). Performance evaluation was based on data from meteorological stations distributed across five climatic zones within the basin, covering the period from 1982 to 2023. The study results showed that the uncertainty-related biases in CMIP6 models limit the accuracy of climate projections in the basin. Using individual GCM models instead of relying solely on linear trends can reduce bias and uncertainty in estimating climate variables across different regions. Although the SDSM model provides a good fit with GCM outputs, the variability in spatial patterns and emission scenarios is reflected in the limited accuracy of some climate indices. Applying bias correction using the linear scaling method has been shown to improve the accuracy of statistical modeling. Projections indicate that rising temperatures due to increased greenhouse gas emissions may decrease rainfall in arid regions while increasing in humid regions. These results contribute to a better understanding of climate change's impacts on water resources in river basins and provide a scientific basis for developing effective adaptation strategies and future planning.