Quantifying the impact of urbanization and climate change on event-based extreme precipitation in Guangdong Province, China

Abstract

Study region Guangdong is located on the southern coast of mainland China and is the province with the highest level and fastest pace of urbanization in the country. Study focus This study investigates how five characteristics of hourly event-based extreme precipitation (HEEP) vary over space and time in Guangdong Province from 1973 to 2021. These characteristics are frequency, total amount, duration, intensity, and temporal unevenness, which is measured by the Gini coefficient. HEEP events, defined by hourly precipitation exceeding the 95th percentile with inter-event times under 6 h, were analyzed using nonstationary Generalized Additive Models for Location, Scale and Shape (GAMLSS). The approach uses covariates to assess impacts, including urbanization metrics derived from land use data on impervious surfaces (such as percentage of landscape (PLAND), patch density (PD), largest patch index (LPI), and aggregation index (AI)) and climate metrics (such as Niño3.4, PDO, IOD, and EASM). New hydrological insights for the region Results reveal distinct spatial patterns: higher frequency in the north, greater total amounts and intensity in the Pearl River Delta (PRD) and western coast, longer durations in the east, and more temporally concentrated precipitation in the PRD. Nonstationary models significantly outperform stationary ones, confirming temporal changes in HEEP characteristics. Urbanization exerts a stronger overall influence, with mean relative contributions of 47.0% (total amount), 43.7% (duration), and 40.7% (Gini coefficient) across stations, whereas the mean urbanization contributions are lower for frequency (26.8%) and intensity (32.7%), indicating a more balanced influence between urbanization and climate drivers for these two characteristics. Notably, AI and PDO enhance frequency, PLAND suppresses it, LPI and AI amplify total precipitation, and Niño3.4 increases temporal unevenness. These effects vary with local urbanization rates, highlighting the need to integrate nonstationarity and multifactor drivers in assessing extreme precipitation under urban expansion and climate change.