The mountainous regions of Southwest China, where Chongqing Municipality is located, has typical regional environmental characteristics such as cloudy fog and less sunshine. In order to realize the spatial simulation of temperature in this geographical environment, this study proposes a model for local regression considering terrain correction factor for solar radiation. In this model, the terrain correction factor is derived indirectly by fitting the spatial distribution of global solar radiation under undulating terrain. The model combines the Geographically Weighted Regression model, the Solar Analyst model, the improved Angtrom-Prescott equation, and the multiple linear regression method. Based on temperature, relative humidity, sunshine percentage, and global solar radiation of the meteorological stations, combined with DEM data with a resolution of 100 m×100 m, this model realizes the spatial simulation of temperature under the mountainous terrain. The model has good fitting accuracy and stability. The simulation accuracy of local regression term is much higher than Inverse Distance Weighting (IDW) interpolation and Kriging interpolation. It is also better than the traditional Multivariate Llinear Regression model based on latitude, longitude, altitude, sunshine percentage, and relative humidity. Further, 55 regional meteorological stations are used to verify the summer temperature simulation accuracy of a single year. The average absolute error is 0.59°C, and the errors of 38 meteorological stations are reduced after considering the terrain correction factor. The model performs well in spatial and temporal simulation of air temperature, which can reflect the influence of local terrain factors such as slope, aspect, and topographic occlusion on temperature, and has clear physical meaning. Based on the available observation data of meteorological stations, DEM, and the commercial software ArcGIS, this model is convenient to apply, especially suitable for cloudy, sunless areas like Chongqing and its surrounding mountainous regions.