Spatiotemporal evolution characteristics of extreme temperature and influenced factors in Guizhou Province from 1960 to 2020

Abstract

[Objective] The frequency and duration of extreme climate events in Guizhou Province are intensified by its unique topography and diverse climate. However, research on extreme temperatures in the province is incomplete. This study aims to understand the spatiotemporal characteristics of extreme temperature indices and their main influencing factors using temperature data from 29 meteorological stations and large-scale climate indices(PDO, ENSO, SOI, AMO) from 1960 to 2020.[Methods] The study analyzes the spatiotemporal evolution and future trends of extreme temperatures in Guizhou Province using linear trend analysis, Mann-Kendall trend test, and R/S analysis. Correlation analysis and wavelet analysis are used to explore the relationships between extreme temperature indices and large-scale climate indices.[Results] The findings reveal a decreasing trend in the duration of cold waves and frost days, while monthly extreme minimum and maximum temperatures and summer days show an increasing trend. Abrupt changes in extreme temperature indices were concentrated in the late 20th to early 21st centuries, and this trend is expected to continue in the next five years. Spatially, low temperature indices decrease from west to east, while high temperature indices increase. The cold wave duration index is likely influenced by longer interannual cycles of ENSO and SOI, while the extreme high temperature index is affected by shorter cycles. Additionally, frost days and monthly extreme minimum temperatures are negatively correlated with altitude.[Conclusion] The main factors affecting extreme temperature indices are climate, topography, and human activities. The result provide a scientific basis for disaster prevention and climate impact decision-making.

Keywords

spatial differences / R/S analysis / correlation analysis / extreme temperature index / Guizhou Province / climate change / Mann-Kendall trend test / spatiotemporal variation