Based on the re-forecast and operational data from the second-generation seasonal prediction model of Beijing Climate Center (BCC_CSM1.1m), and the monthly observational precipitation of 66 stations over Fujian province in 1991-2017, the precipitation prediction ability of the Model during the pre-flood season at different lead time was assessed. The metrics of verification used in this study were anomaly correlation coefficient (ACC), temporal correlation coefficient (TCC), mean square skill score (MSSS) and the prediction score (Ps). The system bias correction (BC), the linear regression (LR) and Empirical Orthogonal Function-analogue correction were used to revise the forecast. Results show that: (1) Although there is always a systematic negative bias in the prediction of the climatological precipitation for the pre-flood season over Fujian province at different leading time, the Model can predict the first and second typical modes of the precipitation in the pre-flood period: the uniform distribution in the whole province and the decrease from south to north; (2) The inter-annual variation of ACC skill and Ps scores of the precipitation prediction are prominent, and the MSSS scores are negative due to the systematic negative bias. The high TCC skill can be found in the northern Fujian province; (3) The prediction ability of the model for precipitation improved significantly after being corrected. The average Ps scores in 2011-2017 are 5.9, 3.5, 6.7 and 7.8 points higher than the raw ensemble at LM2 (leading two months) after the BC, LR, EOFL and EOFNL correction, respectively. The average ACC skill scores in 2011-2017 are 0.02, 0.21, 0.12 and 0.11 points higher than raw predictions after the LR correction. There are significant improvements of MSSS scores for the four correction methods, of which the bias correction and the linear regression correction receive positive techniques; (4) In general, the linear regression correction shows more advantages than the other three correction methods.

%U http://www.climatechange.cn/EN/10.12006/j.issn.1673-1719.2020.061