Advances in Climate Change Research (Feb 2022)
Assessment of Central Asian heat extremes by statistical downscaling: Validation and future projection for 2015‒2100
Abstract
Increasing heatwaves and extreme temperatures have recently been observed across Central Asia (CA). Accurately assessing and projecting the changing climate extremes at the local (station) scale required for climate risk management are therefore highly important. However, global and regional climate models often fail to represent the statistical distributions of observed daily extreme variables and hence extremes in complex terrain. In this work, we developed a statistical downscaling (SD) model to project summer daily maximum temperature (Tmax) and heatwave indices for 65 meteorological stations in CA toward 2100. The SD model involves first-order autoregression and multiple linear regression using large-scale Tmax and circulation indices (CIs) as predictors, and the model is cross-validated against historical observations. The local Tmax and heatwave indices are then projected for 2015–2100 driven by the output of a global climate model (CNRM-CM6-1) under four Shared Socioeconomic Pathways (SSP126, SSP245, SSP370, and SSP585). The application of the SD model significantly improves forecasting of the probability distribution (10th/90th percentiles) of Tmax at stations, particularly across mountainous regions. The model also captures interannual variability and the long-term trend in Tmax, consistent with synoptic-scale inputs. SD projections demonstrate strong warming trends of summer Tmax in CA toward 2100 with rates between 0.35–0.64 °C per decade based on the SSP245 and SSP370 scenarios. Consequently, heatwave occurrence is projected to rise by 1.0–5.0 and 2.0–7.0 d per decade under the SSP245 and SSP370 scenarios, respectively, by 2100. Duration, intensity, and amplitude of heatwaves rise at greater rates under higher-emission scenarios, particularly in southeastern CA. The proposed SD model serves as a useful tool for assessing local climate extremes, which are needed for regional risk management and policymaking for adaption to climate change.
