Land surface temperature (LST) is one of the most important factors in the land-atmosphere interaction process. Raw measured LSTs may contain biases due to instrument replacement, changes in recording procedures, and other non-climatic factors. This study attempts to reduce the above biases in raw daily measurements and achieves a homogenized daily LST dataset over China using 2360 stations from 1960 through 2017. The high-quality land surface air temperature (LSAT) dataset is used to correct the LST warming biases especially evident during cold months in regions north of 40°N due to the replacement of observation instruments around 2004. Subsequently, the Multiple Analysis of Series for Homogenization (MASH) method is adopted to detect and then adjust the daily observed LST records. In total, 3.68 × 10 3 effective breakpoints in 1.65 × 10 6 monthly records (about 20%) are detected. A large number of these effective breakpoints are located over large parts of the Sichuan Basin and southern China. After the MASH procedure, LSTs at more than 80% of the breakpoints are adjusted within +/− 0.5°C, and of the remaining breakpoints, only 10% are adjusted over 1.5°C. Compared to the raw LST dataset over the whole domain, the homogenization significantly reduces the mean LST magnitude and its interannual variability as well as its linear trend at most stations. Finally, we perform preliminary analysis upon the homogenized LST and find that the annual mean LST averaged across China shows a significant warming trend 0.22°C (10 yr) −1 . The homogenized LST dataset can be further adapted for a variety of applications (e.g., model evaluation and extreme event characterization).