中文摘要：

本文研究建立2015/2016年极强厄尔尼诺事件下我国动力和统计结合的气候预测模型,并开展2015年夏季和2016年冬季气候我国160个站点和主要区域实时气候预测。夏季降水的实时预测起报于2月,冬季气温的预测起报于10月。研究结果表明,尽管NCEP-CFSv2耦合气候模式能较好预测2015/2016年极强厄尔尼诺事件中海温异常的演变,但对我国160个站点夏季降水和冬季气温预测仍有较大的偏差。因此,基于NCEP-CFSv2耦合模式预测结果,分别建立我国160个站点冬季气温和夏季降水异常的动力和统计结合气候预测模型。同时,利用年际增量预测方法开展我国长江中下游夏季降水和华北冬季气温的区域气候预测。研究结果表明以上预测模型在2015/2016年的实时预测中较NCEP-CFSv2有更好的预测效能。相对于NCEP-CFSv2耦合模式的预测结果,2015年夏季降水距平空间相关系数ACC从0.21提高到0.31（超过0.01信度的显著性水平）,距平同号率提高到60%,2016年冬季气温ACC从0.19提高到0.32（超过0.01信度的显著性水平）,距平同号率提高到75%。

英文摘要：

Real-time seasonal climate prediction was performed in China during the extremely strong El Nino event of2015/2016,through a combination of dynamical and statistical climate prediction. Generally,real-time summer（ winter） climate prediction in China starts in February（ October） in every year. The results showed that,although the NCEP-CFSv2 coupled model predicted the evolution of the extremely strong El Nino event in 2015/2016 well,its performance in predicting the summer rainfall anomaly of 2015 and the winter temperature of 2016 at 160 stations in China was limited. Compared to observation,CFSv2 predicted a stronger East Asian summer monsoon and weaker East Asian winter monsoon. One of the reasons for this is that CFSv2 is poor at predicting the extratropical climate system. Thus,based on the climate prediction direct outputs of the NCEP-CFSv2 model,we created a hybrid dynamical and statistical prediction model for forecasting the precipitation anomaly and temperature anomaly at 160 stations in China in 2015/2016. The skill of the hybrid of statistical and dynamical prediction model was higher than that of the direct prediction results of the NCEP-CFSv2 model. The spatial anomaly correlation coefficient（ ACC） of summer rainfall at 160 stations in China in 2015 increased from 0. 21 to 0. 31（ exceeding the 99% significance level）,along with the percentage of the same sign of the rainfall anomaly improving to60% from 50%. The model reproducedthe observed flood pattern in southern China,as well as the drought pattern in summer 2015. M eanwhile,the prediction ACC of winter temperature in China in 2016 increased to 0. 32 from 0. 19,and the percentage of the same sign of the temperature anomaly increased to 75% from 62%. M oreover,the year-to-year increment prediction method proposed by Fan et al.（ 2007） was applied successfully to predict summer rainfall over the Yangtze River valley in 2015,and winter temperature over North China in 2016. The year-to-year increment method predicts the year-to-yea