Assessment of CMIP6 in simulating precipitation over arid Central Asia

Precipitation outputs from 30 Global Circulation Models (GCMs) of the Coupled Model Inter-comparison Project Phase 6 (CMIP6) were evaluated from 1951 to 2014 over six climate zones in arid Central Asia (ACA) using the Climate Research Unit TS 4.04 (CRU) precipitation datasets as reference. An evaluation framework was constructed taking into account metrics of annual precipitation patterns, annual cycle precipitation statistics, categorical validation and long-term precipitation trend. The performance of GCMs vary from region to region. Compared to CRU, the 30 selected GCMs present considerable wet bias in the Southern Xinjiang (SX) and Hexi Corridor (HC) regions which can be larger than 100%. The simulated precipitation from most GCMs shows a consistent annual cycle shape and closer with that of observations in the western ACA, but fails to capture precipitation peak in NK and NX regions. Compared to NK, CD, TM and NX, all models and the full model ensemble have limited ability in reproducing the Probability Density Function (PDF) of observations over SX and HC. Most GCMs show limited competence to reproduce the long-term precipitation trend with high wet bias over the Hexi Corridor. Based on the comprehensive performance ranking, it is discovered that CESM2, CESM2-FV2, CESM2-WACCM and CESM2-WACCM-FV2 from NCAR, ACCESS-CM2 from CSIRO and CanESM5 from Canadian Centre for Climate Modeling demonstrate better performances across ACA. The ensemble of 30 selected GCMs has limited ability to accurately simulate precipitation according to the above four types of metrics. The results of this paper may provide scientific guidance to CMIP6 end-users when selecting the most suitable GCMs for their specific applications over ACA.

Automated summary

Evaluation of precipitation outputs from 30 Global Circulation Models (GCMs) over arid Central Asia (ACA) revealed varying performance and wet bias issues in different regions. Models like CESM2, ACCESS-CM2, and CanESM5 demonstrated better performance across the ACA climate zones. The ensemble of 30 selected GCMs had limited ability to accurately simulate precipitation according to multiple metrics.