We analyzed the spatiotemporal variations in surface air temperature and key climate change indicators over the Tibetan Plateau during a common valid period from 1979 to 2018 to evaluate the performance of different datasets on various timescales. We used observations from 22 in-situ observation sites, the CRA-40/Land (CRA) reanalysis data-set, the China Meteorological Forcing Dataset (CMFD), and the ERA-Interim (ERA) reanalysis dataset. The three datasets are spatially consistent with the in-situ observations, but slightly underestimate the annual mean surface air temperature. The daily mean surface air temperature estimated by the CRA, CMFD, and ERA datasets is closer to the in-situ observations after correction for elevation. The CMFD shows the best performance in simulating the annual mean surface air temperature over the Tibetan Plateau, followed by the CRA and ERA datasets with comparable per-formances. The CMFD is relatively accurate in simulating the daily mean surface air temperature over the Tibetan Plateau on an annual scale, whereas both the CRA and ERA datasets perform better in summer than in winter. The in-creasing trends in the annual mean surface air temperature over the Tibetan Plateau from 1979 to 2018 reflected by the CRA dataset and the CMFD are 0.5°C (10 yr)?1, similar to the in-situ observations, whereas the warming rate in the ERA dataset is only 0.3°C (10 yr)?1. The trends in the length of the growing season derived from the in-situ ob-servations, the CRA, CMFD, and ERA datasets are 5.3, 4.8, 6.1, and 3.2 day (10 yr)?1, respectively. Our analyses suggest that both the CRA dataset and the CMFD perform better than the ERA dataset in modeling the changes in surface air temperature over the Tibetan Plateau.