Abstract: This article focuses on the forecasting of daily maximum and minimum temperatures, as well as hourly temperatures. It utilizes different forecast start times of the ECMWF model for the same time period, early-stage forecast errors of the ECMWF model, the evolution of actual temperatures in the previous 24 hours, and spatial information used to distinguish between stations (grid points) as forecast factors. The approach combines spatiotemporal stacking with the XGBoost method to develop separate forecast models for each temperature variable, achieving a temperature correction forecast model that reduces the time scale to hourly forecasts and transforms from station modeling to grid point forecasting. The results of the independent sample experiment for one year show that (1) the accuracy of the corrected daily maximum and minimum temperature forecast has significantly improved compared to the temperature guidance forecast product (NMC) of the China Meteorological Administration. The forecast products for 08: 00 and 20: 00 have increased by 28.6%, 23.2%, 25.5%, and 16.9%, respectively. (2) In the hourly temperature correction forecast, both forecast products have larger forecast errors around 16:00 when the daily temperature peaks. However, in most forecast periods, the XGBoost model shows better forecast accuracy than the NMC forecast product. Moreover, case analysis of the forecast shows that the new method has a lag problem for more pronounced weather changes. Currently, the temperature forecasting correction method has been implemented for operational use.