LAUSR

We demonstrate that there is significant skill in the GloSea5 operational seasonal forecasting system for predicting June mean rainfall in the middle/lower Yangtze River basin up to four months in advance. Much of the rainfall in this region during June is contributed by the mei-yu rain band. We find that similar skill exists for predicting the East Asian summer monsoon index (EASMI) on monthly time scales, and that the latter could be used as a proxy to predict the regional rainfall. However, there appears to be little to be gained from using the predicted EASMI as a proxy for regional rainfall on monthly time scales compared with predicting the rainfall directly. Although interannual variability of the June mean rainfall is affected by synoptic and intraseasonal variations, which may be inherently unpredictable on the seasonal forecasting time scale, the major influence of equatorial Pacific sea surface temperatures from the preceding winter on the June mean rainfall is captured by the model through their influence on the western North Pacific subtropical high. The ability to predict the June mean rainfall in the middle and lower Yangtze River basin at a lead time of up to 4 months suggests the potential for providing early information to contingency planners on the availability of water during the summer season. 本文证明GloSea5季节预报业务系统对长江中下游流域的6月平均降水的预报水平可以达到提前4个月。由于该区域6月降水大部分是由梅雨雨带导致的,因此可以用东亚夏季风指数(EASMI)作为代用指标来间接表征该区域降水。并且我们发现对月时间尺度上的东亚夏季风指数(EASMI)的预报水平也是类似的。然而,研究表明通过预报EASMI来间接的预报月平均区域降水与模式直接预报月平均区域降水相比,预报水平并没有明显提升。6月平均降水的年际变化会受到天气和季节内变率的影响,而这些变率在季节预测的尺度上是不具有可预报性的。模式对月平均降水的预报能力主要是基于模式能够较好的抓住来自前冬赤道太平洋海温的影响,而后者主要是通过影响西北太平洋副热带高压来影响长江中下游6月的降水。能够提前4个月对长江中下游流域6月平均降水进行预报,这意味着可以提早为决策者提供关于夏季可获得水量的信息。