Characterization of compound hot and dry extremes in China based on statistical and dynamical downscaled climate projections

Abstract

The increasing severity and frequency of compound extreme events are concerning consequences of accelerating global warming, particularly the rise in concurrent hot and dry extremes in China. While previous studies have used coarse-resolution global climate models (GCMs) to estimate the likelihood of these compound extremes, this study employs both statistical downscaling dataset and dynamical downscaling with the RegCM5 regional climate model, both driven by the same GCMs, to evaluate the performance in characterizing compound hot and dry extremes across China. To comprehensively assess hot extremes, the analysis mainly focuses on wet-bulb globe temperature (WBGT), which provides a more holistic measure of heat stress than air temperature alone. Meteorological drought is assessed using the widely adopted Standardized Precipitation Index (SPI) and Palmer Drought Severity Index (PDSI). To analyse the changes in the interdependence between hot and dry extremes, this study applies several methods, including the leverage of a sophisticated bivariate copula function to analyse the changes in the dependence structure between hot and dry extremes. The study demonstrates that the statistical downscaling dataset effectively captures the joint probability distribution of hot and dry days; however, it exhibits limitations in fully representing extreme index values. In contrast, the dynamical downscaling approach has the potential to address these limitations. The Pearl River Delta and Yangtze River Delta regions are identified as hotspots susceptible to compound extremes. Over these regions, future joint return periods are projected to decrease intensively, and some unprecedented extreme hot and dry days may become the new norm under both low and high greenhouse gas emission scenarios, posing heightened risks for the local population. These insights can inform more robust climate risk assessment and adaptation planning.

Date of Award	2024
Original language	English
Awarding Institution	The Hong Kong University of Science and Technology
Supervisor	Eun Soon IM (Supervisor)