Dynamical downscaling for regional climate projection in Central Asia based on bias-corrected multiple GCMs

Abstract Central Asia (CA) is among the most vulnerable regions to climate change due to the fragile ecosystems, frequent natural hazards, strained water resources, and accelerated glacier melting, which underscores the need to achieve robust projection of regional climate change. In this study, we applied three bias-corrected global climate models (GCMs) to conduct 9km-resolution regional climate simulations in CA for the present (1986–2005) and future (2031–2050) periods. Dynamical downscaling based on multiple bias-corrected GCM outputs obtains numerous added values not only in reproducing the historical climate but also in projecting the climate changes in CA, in comparison to the original GCMs. The regional climate model (RCM) simulations indicate significant warming over CA in the near-term future, with the regional mean increase of annual daily mean temperature (Tmean) in a range of 1.63–2.01℃, relative to the present period. This increase is expected to be higher north of ~ 45°N in each season except summer and the high-elevation areas have a weaker warming signal than the plains through the year. The season with the largest warming rate is not consistent among the RCM simulations, highlighting the necessity of using multiple GCMs as the boundary conditions to give a range of the projected climate changes. A slight increase in annual precipitation is consistently projected in most plain areas, although the changes over few areas are statistically significant. The climate projections presented here serve as a robust scientific basis for assessment of future risk from climate change in CA.