Quantifying Long-Term Spatiotemporal Variation in and Drivers of the Surface Daytime Urban Heat Island Effect in Major Chinese Cities: Perspectives from Different Climate Zones

Abstract

The urban heat island (UHI) effect and its associated extreme weather events have adverse impacts on human environment-coupled systems. However, the spatiotemporal variations in the UHI effect, as well as potential influencing factors, across climate zones remain poorly understood. This study explored how climate zones influenced the spatiotemporal variation in, trends in, and drivers of summer daytime surface UHI intensity (SUHII) in 220 Chinese cities located in five climate zones from 2000 to 2020. SUHII was quantified using MODIS land surface temperature (LST) data and remote sensing-derived urban built-up area masks were used to quantify SUHII. The Mann–Kendall test was applied to detect long-term SUHII trends, while Pearson correlation and stepwise multiple regression analyses were performed to identify key climatic and geographic drivers across different climate zones. The results indicated summer daytime SUHII values of 1.75 °C ± 1.19 °C, 1.74 °C ± 0.81 °C, 2.37 °C ± 0.75 °C, 2.14 °C ± 1.00 °C, and 2.36 °C ± 0.91 °C for the middle temperate zone (MTZ), south temperate zone (STZ), north subtropical zone (NSZ), middle subtropical zone (MSZ), and south subtropical zone (SSZ), respectively. In most cities, the SUHII increased significantly over time (p < 0.05). Pearson’s correlation analysis indicated that the enhanced vegetation index (EVI) and net radiation (NR) were moderately correlated with the SUHII in the MTZ, with correlation coefficients (r) of 0.465 and 0.42 (p < 0.05). Using a multivariate stepwise regression model, the relative contributions of various influencing factors to the UHI effect were quantified, explaining 27.1% to 57.2% of the variation across different climate zones. In particular, the economic vulnerability index and population density were the main factors affecting the SUHII in the MTZ and SSZ. Our findings support the development of policies aimed at mitigating the UHI effect by addressing the specific requirements of different climate zones to reduce.