Evaluation and multimodel projection of seasonal precipitation extremes over central Asia based on CMIP6 simulations

Abstract

Central Asia faces an increasing challenge related to water resources arising from severe droughts and floods that have impacted the region in the past decades. Using a comprehensive set of extreme precipitation indices, we assess the performance of CMIP6 models in representing observed precipitation extremes and investigate future responses of these extremes to greenhouse gas emissions under four shared socioeconomic pathways (SSP). Particularly, this study identifies robust signals of projected changes in spring and summer precipitation extremes over central Asia. Results show that the CMIP6 models reasonably reproduced the spatial distribution and variability of precipitation extremes over southern central Asia (SCA) and northern central Asia (NCA) during the spring and summer seasons, respectively. Across the SSPs, the CMIP6 models project a decrease in total spring wet‐day precipitation amount (PRCPTOT) over SCA and a significant increase in PRCPTOT over the NCA. The projected changes are characterized by a significant increase in maximum consecutive 5‐day precipitation (RX5day), wet‐day intensity (SDII), and the number of heavy precipitation days (R10mm); these suggest that the spring will be characterized by intense precipitation extremes. Moreover, no significant change is projected for consecutive wet days (CWD) and dry spells (CDD) over SCA. Nonetheless, the CMIP6 models project a significant increase in summer PRCPTOT over SCA and a decrease in summer PRCPTOT, RX5day, R10mm, SDII, and CWD over NCA while projecting a significant and robust increase in dry spells. It is also found that the severity of the projected dry spell deepens across the SSP spectra and gets more pronounced towards the end of the 21st century. Notably, the intermodel spread of the precipitation extremes is smaller during the spring over SCA, larger during the summer season over NCA, and more probable in the warmer future.

Evaluation of CMIP6 Models and Multi-Model Ensemble for Extreme Precipitation over Arid Central Asia

“…Zhang and Wang [39] reported that extreme precipitation in CA is expected to increase approximately linearly with increasing global warming. It has also been projected that seasonal extreme precipitation in southern CA follows an opposite trend than that in northern CA [40].…”

“…Previous studies have shown that GPCC is the most accurate in the global gridded precipitation dataset for average and extreme precipitation in Central Asia, outperforming other precipitation products on both the daily and annual scales [41][42][43][44]. As a result, it is widely used for precipitation and extreme precipitation studies in Central Asia [39,40,45]. The structure of this paper is as follows: The second section introduces the reference dataset and the CMIP6 models dataset used in this paper as well as the research methods.…”

The trend and spatial spread of multisectoral climate extremes in CMIP6 models

“…Dike et al 17 evaluated seasonal precipitation extremes using CMIP6 and reported an increase in the severity of projected dry spells over Central Asia. Collazo et al 18 considered CMIP6 models' ability to represent observed extreme temperatures and concluded that the chosen models could not simulate cold days trends.…”

Extreme Precipitation Events Variation and Projection in the Lancang-Mekong River Basin Based on CMIP6 Simulations

“…In response to the uncertainty of CMIP6 single mode estimation, this study applies the Multi-Model Ensemble (MME) to eliminate the influence of the "unicity" and "uncertainty" of CMIP6 single model prediction. MME is a method that utilizes the results of multiple model simulation and further ensemble average to reduce the uncertainty of model simulation, which is widely used in model simulation [32,33]. Therefore, this paper uses CMIP6 precipitation data averaged by Multi-Model Ensemble to predict the development trend of future extreme precipitation events in the Lancang-Mekong Basin.…”