Abstract: Objective precipitation forecast is a difficult problem in NWP products interpretation.Because of itscharacteristics, objective precipitation forecast is a categorical forecast rather than precipitation amountforecast.The differences between two kinds of categorical precipitation forecast are analyzed.One categorical forecast is based on probability regression.Its method is processing original precipitation to 0 and 1corresponding categories, and then developing forecast equations of different categories to calculate the criterions.In real forecasting, the categorical precipitation will be determined through the criterion and theprobability forecast of that category.The other forecast is based on regression, the method of which ispreprocessing original samples with value smaller than the threshold to category of 0, and then developingforecast equations and criterions.The experimental result from autumn of 2007 to summer of 2008 indicates that probability regressionprecipitation categorical forecast is better than regression precipitation categorical forecast.Especiallywhen forecasting light rain, the TS score averaged over China using probability regression method is higher than that of regression precipitation categorical forecast, the false alarm ratio is obviously smaller, andalso the forecast bias is closer to 1.Through the analysis of predictors and variance contribution of singlesample, the cause of these differences becomes obvious.In regression categorical forecast, the variancecontribution of a few heavy rain samples is too large.It results in the relation of predictors and precipitation mainly reflected those minority heavy rain samples.That is why the false alarm ratio of regression categorical forecast is too high.It can be shown in comparing analysis that the probability regression categorical precipitation forecast is better than direct model precipitation forecast and the situation that false alarmratio is too high is improved.