Inconsistent variation of return periods of temperature extremum in China and its projection based on CMIP6 results

Abstract

Increasingly extreme temperature events under global warming can have considerable impacts on sectors such as industrial activities, health, and transportation, suggesting that risk for these kinds of events under climate change and its regional sensitivity should be reassessed. In this study, the observation and multi-model simulations from CMIP6 are comprehensively used to explore the regional differences of the extreme temperature response to climate change from the perspective of return period (RP). The Gumbel model of generalized extremum distribution is applied to estimate the RP for the annual extremum of temperature based on Gaussian distribution of daily temperature. The analysis on the observation in selected three sites indicates that the regional inconsistency of RP variation is not only existed in extreme high temperature (HTx) but also in low temperature (LTn) during the past several decades. The annual amplitude of temperature extremum in the Northeast China is enlarged with summer becoming hotter and winter becoming colder while the opposite situation is detected in Huang-Huai River Basin with cooler summer and relatively stable winter, and South China is characterized by hotter summer and slight warmer winter. From the spatial distribution of the HTx and LTn variations of fix RP, it is found that the Northeast China and Jiang-Huai River Basin is the most sensitive areas, respectively, in the response of extreme low temperature and high temperature to global warming. However, the regional inconsistency of the extreme temperature change is only observed under SSP1-2.6 scenario in the CMIP6 simulation but gradually disappeared from SSP2-4.5 to SSP5-8.5.

Assessment of Drought Hazard Change in China under 2 ℃, 3 ℃ and 4 ℃ Temperature Rise Scenarios Based on CMIP6 Models

“…Some studies have pointed out that compared with CMIP5, CMIP6 has improved the simulation of extreme events (Chen et al, 2020;Fan et al, 2020;Luo et al, 2020). Therefore, some researchers have analyzed the characteristics and changes in extreme climate in different regions of the world in the future based on CMIP6, such as extreme precipitation (Dike et al, 2022;Tang et al, 2021;Wu et al, 2022;Xu et al, 2022), extreme temperature (Babaousmail et al, 2022;Kuang et al, 2021;Rajulapati et al, 2022;Zhao et al, 2021), heat waves (Zachariah et al, 2021) and drought (Chen et al, 2021;Cook et al, 2020;Naderi et al, 2022;.…”