This study used remotely sensed and assimilated data to estimate all-sky land surface air temperature (T_a) using a machine learning method, and developed an all-sky 1 km daily mean land T_a product for 2003–2019 over mainland China. Validation results demonstrated that this dataset has achieved satisfactory accuracy and high spatial resolution simultaneously, which fills the current dataset gap in this field and plays an important role in studies of climate change and the hydrological cycle.