Assessment of total and extreme precipitation over central Asia via statistical downscaling: Added value and multi-model ensemble projection

Central Asia (CA) is highly sensitive and vulnerable to changes in precipitation due to global warming, so the projection of precipitation extremes is essential for local climate risk assessment. However, global and regional climate models often fail to reproduce the observed daily precipitation distribution and hence extremes, especially in areas with complex terrain. In this study, we proposed a statistical downscaling (SD) model based on quantile delta mapping to assess and project eight precipitation indices at 73 meteorological stations across CA driven by ERA5 reanalysis data and simulations of 10 global climate models (GCMs) for present and future (2081-2100) periods under two shared socio-economic pathways (SSP245 and SSP585). The reanalysis data and raw GCM outputs clearly underestimate mean precipitation intensity (SDII) and maximum 1-day precipitation (RX1DAY) and overestimate the number of wet days (R1MM) and maximum consecutive wet days (CWD) at stations across CA. However, the SD model effectively reduces the biases and RMSEs of the modeled precipitation indices compared to the observations. Also it effectively adjusts the distributional biases in the downscaled daily precipitation and indices at the stations across CA. In addition, it is skilled in capturing the spatial patterns of the observed precipitation indices. Obviously, SDII and RX1DAY are improved by the SD model, especially in the southeastern mountainous area. Under the intermediate scenario (SSP245), our SD multi-model ensemble pro-jections project significant and robust increases in SDII and total extreme precipitation (R95PTOT) of 0.5 mm d-1 and 19.7 mm, respectively, over CA at the end of the 21st century (2081-2100) compared to the present values (1995-2014). More pronounced increases in indices R95PTOT, SDII, number of very wet days (R10MM), and RX1DAY are projected under the higher emission scenario (SSP585), particularly in the mountainous southeastern region. The SD model suggested that SDII and RX1DAY will likely rise more rapidly than those projected by previous model simulations over CA during the period 2081-2100. The SD projection of the possible future changes in precipitation and extremes improves the knowledge base for local risk management and climate change adaptation in CA.

Automated summary

Central Asia is highly sensitive and vulnerable to changes in precipitation due to global warming. In this study, a statistical downscaling model based on quantile delta mapping was used to assess and project precipitation extremes in the region. The model effectively reduced biases and adjusted the distributional biases in the downscaled daily precipitation and indices, and accurately captured the spatial patterns of observed precipitation indices.