Forecast verification involves exploring and summarizing the relationship between sets of forecast and observation and making comparisons between the performances of different forecasting systems.Statistical significance is one important aspect of measuring the absolute quality of verification results.It is an effective way to judge whether the performance improvement is statistically significant or just arisen by chance.For general meteorological forecast verification,some verification scores,such as precipitation skill scores,can hardly use the standard procedure for confidence interval to measures the difference in performance between different forecast systems.It is not possible to be sure that the apparent differences in skill scores are real and not just due to random fluctuations because of the data uncertainty.The Monte Carlo method is a numerical way to account for this.By resampling process,we can provide an adequate representation of the full underlying population which satisfies normal distribution by the verification samples of random variables.In this paper,some precipitation skill scores of GRAPES global forecast system and T639 models such as Threat Score and Bias Score are calculated from 1Aug to 31 Aug of 2015.The daily precipitation observation data are taken from 2 400 Chinese rain gauges.The Monte Carlo method is used for a statistical significance test and the convergence characteristics with different resampling times are also analyzed.Results show that Monte Carlo test using 10 000 test samples looks sufficient and a real model performance with significant improvement is provided.