The mesoscale operational numerical model known as Global and Regional Assimilation and Prediction System GRAPES-MESO v3.0, from the National Meteorology Center of the China Meteorological Administration (CMA), was used to study the application of a one-dimensional variational (1DVAR) surface-precipitation assimilation scheme in the GRAPES-three-dimensional variational (3DVAR) data assimilation system (Expt ASSI) by using the experimental forecasts of the period June 1-30, 2010. The results are then compared with those of experiments without rainfall assimilation (Expt CNTL) to evaluate the application effects of assimilating 1 h intensive nationwide rainfall data into the GRAPES-3DVAR. The results are summarized in the following points: 1) 1DVAR precipitation assimilation can provide a meaningful modification for the moisture profiles by providing rainfall analysis results that are close to those determined through observation in the limits of moisture background errors and rainfall observational errors. The initial fields were obviously improved in Exp ASSI, in which the temperature, pressure, moisture, and wind values were modified to be closer to the observed values. 2) For the continuous precipitation process south of the lower reaches of the Yangtze River and in South China during June 17-21, 2010, the daily rainfall and hourly precipitation forecast of Exp ASSI was generally stronger than that of Exp CNTL and were closer to the observed values. 3) The threat score (TS) and equitable threat score (ETS) of 0-24 h rainfall forecasts at 0800 BT from Exp ASSI were better than those from Exp CNTL for rainfall levels of 1 mm, 10 mm, 25 mm, 50 mm, and 100 mm. Moreover, its forecasting bias is much closer to 1.0. The TS and ETS of 0-24 h precipitation were increased, and the forecast bias was decreased after assimilation of the 1 h accumulated precipitation in the GRAPES-3DVAR. 4) The distribution, evolution, and intensity variation of the rain region in Exp ASSI were better than those of Exp CNTL. 5) The rainfall ETS score for one month and the verification of typical heavy rain cases indicate that the assimilation of surface precipitation data in the GRAPES-3DVAR by using the 1DVAR precipitation scheme can improve the precipitation forecasts of GRAPES-MESO v3.0.