Based on the multi-mode precipitation gridded forecast data, observation data at meteorological stations of Qinghai Province and precipitation gridded analysis product of CMA multi-source precipitation analysis system (CMPAS), the prediction performance of models to heavy precipitation cases in Qinghai Province from July to August 2020 were comparatively tested by using traditional verification method such as threat score (TS) and spatial verification method such as fraction skill score (FSS) of neighborhood method and object-oriented diagnostic evaluation method (MODE). The main conclusions are as follows: (1) The traditional TS scores of global model of European Center of Medium-range Weather Forecasts (ECMWF) and National Center for Environmental Prediction (NCEP), China Meteorological Administration global assimilation forecast system (CMA-GFS) and GRAPES regional mesoscale numerical prediction system (GRAPES-Meso) to light rain and above were higher, and the prediction performance difference of four models to light rain was little, but the models with the highest score under different verification methods were slightly different. (2) Compared with the observation, the forecasted locations of four models to moderate rain and above were generally to the west. The traditional TS scores of moderate rain and above were significantly different, but the performance score of models under different verification methods was relatively consistent. (3) Compared with the observation, the forecasted location of four models to heavy rain and above was generally to the north. The prediction ability of each model to heavy rain and above was poor, and the traditional TS scores of heavy rain and above were equal to 0, while FSS scores could effectively improve the evaluation ability to models difference, and MODE could give the specific performance of corresponding object attributes, which provides valuable reference for model application, but it was more sensitive to the selection of verification parameters.