Comparison and evaluation of the performance of reanalysis datasets for compound extreme temperature and precipitation events in the Qilian Mountains

Abstract

Due to potential powerful destruction, the compound extreme temperature and precipitation events have continuously captured the interest of scientists. However, the lack of in-situ datasets limits the knowledge of compound extreme events in the alpine mountains. We selected the Qilian Mountains and analyzed the change trends of four types of compound extreme weather events, and further evaluated the performances of five reanalysis datasets for compound extreme weather events based on the observed daily datasets from 33 meteorological stations from 1980 to 2020. The results showed that (1) The frequency of four compound extreme weather events from highest to lowest is cold-dry events, high temperature-dry events, cold-heavy precipitation events, and high temperature-heavy precipitation events. High temperature-heavy precipitation events and high temperature-dry events showed increasing trends, while cold-dry events and cold-heavy precipitation events showed decreasing trends. (2) All five reanalysis datasets underestimated temperature and overestimated precipitation. In terms of extreme temperature and extreme precipitation events, ERA5-Land performed best, followed by ERA5, Har v2 and Merra2, and JRA55 performed worst. (3) For the four types of compound weather events, the reanalysis datasets performed better for high-temperature or dry events than cold or heavy precipitation events in the Qilian Mountains. The reanalysis datasets performed best for compound high temperature-dry events and worst for compound extreme temperature-heavy precipitation events. According to the results, we recommend ERA5-Land and ERA5 for the research on the compound extreme events. The research results provide theoretical support for the study of compound extreme temperature-precipitation events in alpine regions with sparse in-situ datasets.