Surface air temperature (T_a) is critical to the studies of radiation balance, energy budget, and water cycle. It is a necessary input for associated models. Most of the current T_a datasets of reanalysis products have limitations at local scales due to their coarse spatial resolutions. For better modeling the radiation balance, energy budget, and water cycle over the Tibetan Plateau, this study proposes a practical method for T_a downscaling based on the digital elevation model. This method is applied to downscale T_a of the China regional surface meteorological feature dataset (CRSMFD) at 0.1° and the ERA-interim (ERAI) product at 0.125° to 0.01°. The daily mean T_a and the 3-hourly instantaneous T_a with a 0.01° are obtained. The downscaled T_a are evaluated from the perspectives of accuracy and image quality. Results show that the daily mean T_a downscaled from the CRSMFD product has a RMSE of 1.13 ± 1.0 K at 105 meteorological stations and RMSEs of 0.96 K to 2.34 K at three experimental stations; the instantaneous T_a downscaled from CRSMFD has RMSEs of 1.02 K to 4.0 K at the three experimental stations. T_a after downscaling has better agreement with the ground measured T_a than before downscaling, especially in mountain areas. By contrast, T_a downscaled from the ERAI product has unacceptable accuracy due to the great uncertainty of the ERAI T_a over the Tibetan Plateau. With the proposed method, a 0.01° T_a dataset from 2000 to 2015 over the Tibetan Plateau was generated to satisfy related studies and applications.