Abstract: A prediction model using LightGBM was established according to a regression analysis using ground observations of temperature, ERA-Interim data and 0-12 h forecast data. Tests on the data set from January to April in 2018 showed that the mean absolute error of the temperature decreased from 1.8℃ to 1.1℃ with the EC model, the prediction accuracy increased from 65.9%to 86.6% and the coefficient of determination was 0.97. This model has been operationalized by the Wuhan Central Meteorological Observatory. The forecast score from February to June in 2018 showed that the accuracy of the highest and lowest temperature forecasts were 76.1% and 91.5%, respectively, which was significantly higher than that of the numerical weather prediction, and the minimum temperatures were better than current forecasters. As a young machine learning frame, LightGBM has strong prospects for application in meteorological forecasting.