The IPCC indicates that warming should include three types of shifted mean, increased variability, and changed symmetry. However, at present, the main concern of warming is still shifted mean, which affects the overall understanding of surface warming. In this study, based on daily temperature data from 590 meteorological stations during 1961—2018, we quantified indicators of shifted mean, increased variability, and changed symmetry using mean temperature, temperature variance, and high-temperature date, respectively. Then we revealed the spatial pattern of trends in mean temperature, temperature variance, and high-temperature date across China. It was found that, although the mean temperature exhibited a significant increasing trend over the whole study period, the trend was reversed around 1986, with a decreasing trend before 1986 and a significant warming trend after 1986. During 1961—2018, the number of stations with an advance in the high-temperature date (63.6%) was greater than that with a delay (36.4%). In addition, the temperature variance showed a significant decreasing trend from 1961 to 1986, but a notable increasing trend from 1986 to 2018. Overall, variation in mean temperature, temperature variance, and high-temperature date had large spatially heterogeneity. From 1961 to 1986, the stations with delayed high-temperature date, decreasing mean temperature, and decreasing temperature variance were the most numerous, accounting for 23.9% of the total, and mainly distributed in the subtropical region; while during 1986 to 2018, the stations showing a delayed high-temperature date as well as increases in mean temperature and temperature variance were the most numerous, accounting for 41.5% of observed stations, with a more scattered spatial distribution. The regional variability in the trend changes of different temperature indicators reflects the heterogeneity of global change sensitivity in different regions. Therefore, studies that simultaneously focus on the changes of mean temperature, high-temperature date, and temperature variance can better reflect the characteristics of climate change and help predict the future climate change risk, which is of great significance to develop mitigation and adaptation policy frameworks for climate change in China.