Abstract: The increasing frequency of extreme climate events has not only directly affected vegetation growth but also posed serious threats to agricultural-pastoral production and ecological sustainability. To facilitate global and regional studies on climate extremes, the Expert Team on Climate Change Detection and Indices (ETCCDI) defined 27 core climate indices. Among these, temperature-related indices are critical for understanding the temporal and spatial characteristics of extreme temperature events and their driving mechanisms, which are essential for regional ecological conservation and agricultural security. However, previous studies mostly focused on station-scale data and lacked systematic investigations at the regional level over longer timescales. Moreover, the causes of extreme climate involve multiple factors and the driving mechanisms remain poorly understood, although recent findings have indicated a strong relationship between their variability and atmospheric circulation patterns. In this study, we investigated the spatiotemporal variation of extreme temperature using 16 temperature-related ETCCDI indices in Inner Mongolia, China. We constructed the annual time series of extreme temperature indices based on ERA5-Land reanalysis dataset on Google Earth Engine (GEE) platform, and utilized Sen’s slope estimator, Mann-Kendall test and Pearson correlation coefficient to comprehensively analyze the spatiotemporal changes in extreme temperatures and the causes of atmospheric circulation in Inner Mongolia from 1951 to 2020. First, meteorological station data were used to validate the daily average temperature, daily maximum temperature, and daily minimum temperature in ERA5-Land reanalysis data to verify the consistency of the ERA5-Land reanalysis data and the meteorological station temperature data. Second, the annual time series of the extreme temperature indices were calculated using ERA5-Land data within the GEE platform, and the Sen slope estimation and Mann-Kendall test methods were used to analyze the spatial distribution and changing trends of extreme temperatures. As the final step, the Pearson correlation coefficient method was employed to analyze the correlation between the extreme temperature indices and the atmospheric circulation indices, thereby identifying the dominant drivers of extreme temperature variability in Inner Mongolia.The results showed that: 1) extreme temperature indices exhibited clear spatial heterogeneity. Frost days and ice days were most frequent in the central Yinshan Mountains and northeastern Greater Khingan Range, whereas summer days and tropical nights were more common in the western desert regions. Maximum values of daily maximum and minimum temperatures were highest in desert areas, while mountainous regions had the lowest. Other indices, such as cold/warm spell durations, diurnal temperature range, and growing season length, also displayed distinct regional patterns, reflecting Inner Mongolia’s complex climatic and geographic features. 2) Most extreme temperature indices demonstrated significant warming trends, with cold indices decreasing and warm indices increasing, indicating a reduction in extreme cold events and an intensification of extreme heat events. 3) Among the four atmospheric circulation indices examined, the Arctic Oscillation (AO) had the most significant impact, contributing to winter warming in northeastern Inner Mongolia. The Pacific Decadal Oscillation (PDO) mainly influenced the western deserts, while the North Atlantic Oscillation (NAO) and Southern Oscillation Index (SOI) showed minimal effects. These findings offer valuable insights for regional ecological management and the sustainable development of agriculture and animal husbandry in Inner Mongolia.