Abstract:Using the smart grid reality analysis product (CLDAS-V2.0) and the European Centre for 〖JP〗Medium-Range Weather Forecast Global Model (EC) product, which were operated at the national level from September 2017 to March 2021, six regions are constructed according to the geographical distribution characteristics of Hubei Province. The temperature forecast model established by LightGBM machine learning algorithm is used to generate 0.05°×0.05° gridded temperature forecast products in Hubei Province, and the forecast products are verified by the forecast products and grid data from April to September 2021. The results show that the temperature prediction method (MLT) based on machine learning has achieved a good forecast effect, and MLT is superior to SCMOC and EC model products in 0-72 h lead time. The error of MLT in mountain area is larger than that in plain area, but the correction amplitude of MLT in mountain area is larger than that in plain area, and the correction amplitude of daily maximum temperature is larger than that of daily minimum temperature. The diurnal variations of MAE in MLT, SCMOC and EC model products from April to September present the characteristics of higher in daytime, lower over night, and convex single-peak in afternoon. The MAE value of MLT is lower than that of SCMOC and EC model products, and still has an advantage in changeable weather. The results of site test and grid test are consistent, and the temperature forecast product based on grid modeling is also corrected. Machine learning can be used as an effective means to correct the pattern of gridded temperature.