A nowcasting method based on blending Doppler weather radar data and mesoscale numerical weather prediction (NWP) model products is presented. The method is as follows: Firstly, by using correlation analysis, position errors are calculated between radar precipitation estimate and precipitation estimated from reflectivity factor from the output of NWP model in this same time, and thus displacement deviation vectors fields are obtained. Then, displacement deviation vector fields are partitioned with human computer interaction and each deviation vector field gets smoothed, so the average displacement deviation vector of each partition is obtained. Finally, the trend variation characteristic of average displacement deviation vector of each partition with time is established by using least square method to linearly fit the continuous time multiple average displacement deviation vectors for each partition, and according to the trend, spatial position deviation of precipitation estimated from reflectivity factor from the output of NWP model is corrected in the future periods. The method was once applied to three severe prceipitation cases in the summers of 2012 and 2013 that happened in the west of Chongqing and the east of Sichuan. The nowcasting verification results show that for the 0-2 h nowcasting, the performance of blending forecasts is generally superior to model forecasts. Compared with quantitative precipitation forecast (QPF) of radar based extrapolation, the performance of radar based extrapolation QPF is superior to blending forecasts in the first hour but the performance of blending forecasts is superior to radar based extrapolation QPF in the second hour.