Changing temporal volatility of precipitation extremes due to global warming

Abstract

It is of utmost importance to understand how precipitation extremes change due to global warming. Here, we examine the volatility of precipitation extremes by analysing the waiting time distribution between events and the clustering of precipitation extremes. For this we use the ERA5 reanalysis data and high-resolution simulations with the Community Earth System Model (CESM). We find significant evidence for a power-law distribution of waiting times between precipitation extremes and of serial clustering of precipitation extremes. This suggests that precipitation extremes do not occur independently from each other. This is in contrast with previous studies which typically assume that precipitation extreme events occur independently from each other. CESM reproduces these properties well. The climate change simulations show that the waiting times between precipitation extremes become shorter and that at the same time the clustering of precipitation extremes increases. Hence, global warming affects the temporal characteristics of precipitation extremes and, thus, precipitation extremes will become more volatile.

Factors Affecting the Spatiotemporal Variation of Precipitation in the Songhua River Basin of China

“…They also observed that heavy rainfall days exhibited a decrease before 2013, and the decreasing trend was alleviated after 2013. Given the spatiotemporal variability of mean and extreme precipitation climatology, several studies have analyzed the reasons for the observed differences [21][22][23][24][25], including geographical factors [26][27][28][29], global warming [30][31][32], circulation factors [11,[33][34][35][36], and urbanization [37,38].…”

Performance of regional climate model RegCM4 with a hydrostatic or non‐hydrostatic dynamic core at simulating precipitation extremes in China

“…Numerous studies have investigated precipitation extremes at global and regional scales from sub‐hour to interannual time scales (Barton et al, 2022; Delgado‐Torres et al, 2023; Dhakal et al, 2023; Jong et al, 2023; Meng et al, 2023; Qin, 2022; Wang et al, 2022), including their connections with the surface air temperature in the Clausius–Clapeyron relationship (Franzke, 2022; Giorgi & Ciarlo, 2022; O'Gorman & Schneider, 2009; Qin et al, 2021; Qin & Shi, 2022; Thackeray et al, 2022), large‐scale atmospheric circulation (Barton et al, 2022; Dhakal et al, 2023) and oceans (Liu, Gao, et al, 2021; Wei et al, 2021). Due to the high internal variability, low spatial resolution and limited temporal length of observations as well as the large uncertainty in model simulations, it is still challenging to study changes in precipitation extremes and their underlying mechanisms in low signal‐to‐noise ratio systems (de Vries et al, 2023; Xu et al, 2022).…”