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As the highest mountain in the southeastern Tibetan Plateau, Gongga Mountain is highly sensitive to climate change which is hardly assessed with limited meteorological stations in this region. To analyze the warming trend of Gongga Mountain, the mean annual surface temperature (MAT) was extracted based on an annual temperature cycle model with the MODIS/Terra daily land surface temperature (LST) product and used to identify the MAT trends from 2000 to 2018 using the Mann‐Kendall trend test. The results indicate that almost all the pixels passing a significance test with the confidence level of 95% show an increasing trend, and the daytime MAT exhibited a stronger warming trend with the mean rate of 0.11 K/yr than the nighttime MAT (0.07 K/yr). However, more pixels (30.2%) exhibited significant warming trends at night than during the daytime (21.6%). Additionally, the significant warming pixels were mainly distributed above 3,200 m, and the number of pixels increased with elevation. Most importantly, the statistics of the daytime and nighttime changing rates at different elevation ranges support elevation‐dependent warming (EDW). These warming patterns are connected to changes in snow and cloud cover and the rate shows good correlations with the changes in clear‐sky days and vegetation cover conditions. In general, this study verifies that remotely sensed LST can be used to analyze the warming trends in mountain areas and identifies the presence of EDW at Gongga Mountain. This trend could result in a strong influence on the local mountain ecosystem functions, water resources, and glacial changes. Key Points: Nighttime land surface temperatures (LSTs) had higher correlations with surface elevation than daytime LSTsLST exhibited a larger advantage than in situ air temperature for depicting the spatial distributions of surface warmingA trend analysis of annual mean LSTs can effectively capture the elevation‐dependent warming (EDW) feature at Gongga Mountain [ABSTRACT FROM AUTHOR]