Abilities of Climate Forecast System version 2 (CFSv2) in predicting seasonal mean precipitation quantities have been demonstrated utility, but it did not address the questions related to the specific character of daily precipitation statistics, which are in high demand of final users. In this study, summertime daily precipitation statistics from the CFSv2 reforecasts were evaluated over East China. The daily precipitation statistics considered included the number of wet days, precipitation intensity (represented mean precipitation), and four extreme precipitation indices. Simulations of the climatological daily precipitation statistics by CFSv2 have been examined. The most significant bias in CFSv2 is that it produces excessive consecutive light and moderate precipitation but misses most short-duration heavy and extreme precipitation events over South and Northeast China, which further limits the simulation of precipitation intensity and precipitation extremes there. Some possible causes for model biases have been discussed. Direct application of CFSv2 in impact modelling studies is hampered by model biases. A simple bias correction of precipitation frequency was used to ensure better agreement with observation. After adjustment of the wet day threshold in CFSv2, simulation of number of wet days and extreme precipitation days were completely corrected. Moreover, simulation of precipitation intensity as well as four extreme precipitation indices all showed improvement.

已经证明气候预报系统第2版（CFSv2）具有预测季节性平均降水量的能力，但它并未解决与最终用户的高需求有关的每日降水统计的特定特征的问题。在这项研究中，对华东地区CFSv2预报的夏季日降水量统计数据进行了评估。所考虑的每日降水统计包括湿天数，降水强度（代表平均降水）和四个极端降水指数。已经检查了CFSv2对气候日降水量统计的模拟。CFSv2最显着的偏差是，它连续产生少量连续的轻度和中度降水，但却错过了华南和东北部大多数短时的重度和极端降水事件，这进一步限制了那里的降水强度和极端降水的模拟。已经讨论了模型偏差的一些可能原因。CFSv2在影响建模研究中的直接应用受到模型偏差的阻碍。使用简单的降水频率偏差校正可确保与观测结果更好地吻合。调整CFSv2中的湿天阈值后，完全校正了湿天数和极端降水天数的模拟。此外，降水强度的模拟以及四个极端降水指数均显示出改善。这进一步限制了那里的降水强度和极端降水的模拟。已经讨论了模型偏差的一些可能原因。CFSv2在影响建模研究中的直接应用受到模型偏差的阻碍。使用简单的降水频率偏差校正可确保与观测结果更好地吻合。调整CFSv2中的湿天阈值后，完全校正了湿天数和极端降水天数的模拟。此外，降水强度的模拟以及四个极端降水指数均显示出改善。这进一步限制了那里的降水强度和极端降水的模拟。已经讨论了模型偏差的一些可能原因。CFSv2在影响建模研究中的直接应用受到模型偏差的阻碍。使用简单的降水频率偏差校正可确保与观测结果更好地吻合。调整CFSv2中的湿天阈值后，完全校正了湿天数和极端降水天数的模拟。此外，降水强度的模拟以及四个极端降水指数均显示出改善。使用简单的降水频率偏差校正可确保与观测结果更好地吻合。调整CFSv2中的湿天阈值后，完全校正了湿天数和极端降水天数的模拟。此外，降水强度的模拟以及四个极端降水指数均显示出改善。使用简单的降水频率偏差校正可确保与观测结果更好地吻合。调整CFSv2中的湿天阈值后，完全校正了湿天数和极端降水天数的模拟。此外，降水强度的模拟以及四个极端降水指数均显示出改善。