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In recent years, past changes in global and regional land surface temperatures (LST) have been well studied, however, future LST changes have been largely ignored owing to data limitations. In this study, three climate variables of CMIP6, namely air temperature (AT), precipitation (Pre), and leaf area index (LAI), were spatially corrected using the Delta downscaling method. On this basis, by combining MODIS LST, elevation, slope and aspect, a random forest (RF) model was built to calculate the LST from 2022 to 2100. The absolute variability (AV) and Mann-Kendall (M-K) tests were used to quantitatively detect interannual and seasonal LST changes in different Shared Socioeconomic Pathways (SSPs) scenarios. The results showed that the AV value increased successively from SSP1-2.6 to SSP2-4.5 and then to SSP5-8.5. Compared with the base period (2003-2021), the increment in interannual, spring, summer and autumn LST during 2022–2100 was mainly between 1 and 2 °C under three scenarios. The interannual and seasonal LST were spatially characterized by significant warming over large areas, and the increasing was the fastest under SSP5-8.5. These results indicate that, in the future, LST will increase further over large areas, especially in winter.

Future variation of land surface temperature in the Yangtze River Basin based on CMIP6 model