The accurate cumulative precipitation forecasts are essential for monitoring water resources and natural disasters. Thecombination of deep learning and big data has become a new direction for precipitation forecasting. However, the currentlarge models are still lacking in-situ data verification. To accomplish this goal, the precipitation forecasting performance of astate-of-the-art model GraphCast was evaluated. Using the cumulative precipitation data from 2393 observation stations for the1-3 day period as a reference, we assessed the cumulative precipitation in mainland China region for the 1-3 day period from2020 to 2021, utilizing a high-resolution model with 0.25◦×0.25◦ grid spacing and 37 layers of parameters. The precipitation ofEuropean Centre for Medium-Range Weather Forecasts (ECMWF) was also compared. The results show that: (1) During the2020-2021 period, for the 1-day, 2-day, and 3-day cumulative precipitation forecasts, the Root Mean Square Error (RMSE)values of GraphCast were primarily between 0.46 to 9.38 mm/d, 0.44 to 9.06 mm/d, and 0.44 to 9.06 mm/d, respectively. TheMean Error (ME) values were mainly between −0.595 to 1.705 (0.01 mm). (2) As the forecast period extends, the forecastingcapability of GraphCast declines. (3) In the 1-3 day cumulative precipitation forecasts for various stations in mainland China,GraphCast demonstrates higher predictive accuracy than ECMWF. (4) Compared to ECMWF, GraphCast demonstrated thebest forecast performance in the warm-temperate humid and sub-humid north China, with the RMSE being approximately 12%higher. Our study indicates that GraphCast demonstrates significant potential and higher accuracy in precipitation forecasting.