Abstract: Using the 24 h precipitation forecasts products from the ECMWF high-resolution numerical model (ECMWF-Hi) and the ECMWF ensemble model (ECMWF-EPS) and the hourly precipitation observations at more than 2 400 national meteorological stations over China from June to August of 2015 to 2017, we have conducted an evaluation of the accuracy, concentration and correlation of 24 h precipitation forecast products from ECMWF-Hi, and compared them with those from ECMWF-EPS. In addition, in order to overcome the shortcoming of describing the concentration simply by the standard deviation ratio or mean value ratio, we have established the R index which combines standard deviation ratio with mean value ratio, and used it to describe the concentration of forecast quantitatively. Results are as follows. (1) ECMWF-Hi does not show an advantage in the verification of root mean square error (RMSE), while ensemble mean of ECMWF-EPS with low horizontal resolution achieves the lowest RMSE. (2) Generally, the ECMWF-Hi is more accurate in description of concentration of precipitation forecast in the research areas. The dispersion of precipitation forecast is similar to that of the observation. The expectation of precipitation forecast of ECMWF-Hi is also the closest to that of the observation. Compared to the ensemble control forecast of ECMWF-EPS and ensemble mean of ECMWF-EPS, ECMWF-Hi is more accurate in concentration of precipitation. (3) The distribution of the R index at sites over the research area indicated that compared to the ensemble control forecast and the ensemble mean of ECMWF-EPS, ECMWF-Hi has more number of stations with under estimation of precipitation, and the concentration of those stations is generally higher than that of the observation. The precipitation forecast of ECMWF-Hi is closer to the observed precipitation. (4) The evaluation results show that the R index can not only evaluate the concentration of the forecasting of precipitation of model qualitatively, but also can describe the concentration quantitatively.