Abstract: The principles, computation and error characteristics of a new method newly developed internationally for precipitation verification, i.e., SEEPS (Stable Equitable Error in Probability Space), are briefly introduced in this paper. With regard to score meaning, precipitation classification, score calculation and application, SEEPS is more flexible and has clearer practical meaning than traditional verification scores. The SEEPS method is applied in the verification and assessment experiment for quantitative precipitation forecasts in China region, and some issues encountered in the application are talked about. Climatic precipitation probability distribution is divided into three classifications, ‘dry’, ‘light’ and ‘heavy’ by two thresholds in SEEPS. Error matrices are determined by the precipitation probability. Area mean is weighed based on the station density. Possessing different error score characteristics under varying precipitation probabilities makes SEEPS automatically adapt to various kinds of precipitation climate. The ability of precipitation forecasts is quantified by the SEEPS values, and different elements of SEEPS can also be analyzed to find the reason why the SEEPS score is high or low. Daily observations of 24-hour accumulated precipitation from Mar. 2007 to Dec. 2013 are used in the SEEPS verification test for quantitative precipitation forecasts (QPF) of forecasters in the National Meteorological Center of CMA. Results show that the two main categories of forecasters’ QPF errors are misses of heavy precipitation with light precipitation forecasts, and false alarms of “dry” with light precipitation forecasts. These two kinds of errors account for about 70 percent of all errors. The former explains the order of forecast precipitation is lower than observations and the latter states that false alarms with large areas and high frequencies can also obviously deteriorate the whole forecast performance. Conclusions of SEEPS are approximately equivalent to those of traditional verification scores. But the SEEPS score is simpler and more suitable for use in management and decision making, and has more value for popularization and application.