Based on the data from the TIGGE datasets of European Centre for Medium-Range Weather Forecasts(ECMWF),Japan Meteorological Agency(JMA),National Centers for Environmental Prediction(NCEP),China Meteorological Administration(CMA) and United Kingdom Met Office(UKMO),the Kalman filter method was applied to conduct multimodel ensemble forecasts of the surface air temperature and precipitation.The results show that the multimodel ensemble forecasts by using Kalman filter are superior to those of the bias-removed ensemble mean(BREM) and other individual models.However,the forecast results of Kalman filter method vary for different meteorological elements and different forecast lead times.For the surface air temperature forecast in China,Kalman filter method shows the best forecast capability while for the precipitation forecast,it has a higher TS score than the BREM.However,with longer forecast lead time,the TS scores for heavy rains are approximately equivalent to those of the best individual model UKMO.So the Kalman filter method does not improve the forecast capability of heavy rains significantly.To sum up,the root mean square error(RMSE) of surface air temperature and precipitation forecasts based on Kalman filter is the smallest among those of the multimodel ensemble forecasts and each individual model forecasts.