Seasonal prediction of western North Pacific summer monsoon rainfall (WNPSMR) is in great demand but remains challenging, because the relationships between the Asian monsoon system and precursors are nonstationary and exhibit significant decadal changes. The present study aims to (1) examine decadal variations of the relationships between the WNPSMR and predictors used in previous studies and (2) establish a new prediction model using a Bayesian dynamical linear model (DLM), which is capable of capturing the time-evolving relationships between the predictand and predictors whereas the conventional static linear model cannot. Two predictors were selected previously to predict the WNPSMR. One is the sea level pressure tendency anomalies over the tropical eastern Pacific from late spring to early summer, which represents remote forcing related to ENSO and has a stable effect on WNPSMR throughout the analysis period. The other is the sea surface temperature anomaly difference between the northern Indian Ocean (IO) and the WNP during spring through early summer (called IOWPSST), which denotes local air–sea interaction that affects the WNP subtropical high. Results show that the IOWPSST has strong influence on WNPSMR during 1979–2003 (period 1), while from 2004 to 2017 (period 2) its connection to WNPSMR evidently weakens. This nonstationary relationship is due to the non-persistence of the enhanced WNP subtropical high during period 2, which is associated with the positive-to-negative phase transition of the Interdecadal Pacific Oscillation since ~ 2000. A new prediction model was established using the two predictors with Bayesian DLM. The cross-validation method and a 9-years independent forward-rolling forecast is applied to test the hindcast and actual forecast ability. Results show that the Bayesian DLM has higher hindcast/forecast skill and lower mean square error compared with static linear model, suggesting that the DLM has advantage in predicting WNPSMR and is a promising method for seasonal prediction.

对北太平洋西部夏季季风降水（WNPSMR）的季节预测需求量很大，但仍具有挑战性，因为亚洲季风系统与前兆之间的关系不稳定，而且年代际变化很大。本研究旨在（1）检查WNPSMR与先前研究中使用的预测变量之间关系的年代际变化，以及（2）使用贝叶斯动态线性模型（DLM）建立新的预测模型，该模型能够捕获时间-预测变量与预测变量之间不断发展的关系，而传统的静态线性模型则不能。先前选择了两个预测变量来预测WNPSMR。一是从春季末到初夏整个热带东太平洋的海平面压力趋势异常，它表示与ENSO相关的远程强迫，并且在整个分析期间对WNPSMR都具有稳定的影响。另一个是春季到初夏期间北印度洋（IO）和WNP之间的海面温度异常（称为IOWPSST），这表示影响WNP副热带高压的局部海-气相互作用。结果表明，IOWPSST在1979-2003年（期间1）对WNPSMR有很强的影响，而从2004年到2017年（期间2），其与WNPSMR的联系明显减弱。这种不稳定的关系是由于在第2阶段期间WNP副热带高压的持续升高所致，这与2000年以来年代际太平洋涛动的正负相变有关。使用这两种方法建立了新的预测模型贝叶斯DLM的预测变量。使用交叉验证方法和9年独立前瞻性预测来测试后验和实际预测能力。结果表明，与静态线性模型相比，贝叶斯DLM具有更高的后验/预测能力和均方误差，说明DLM在预测WNPSMR方面具有优势，是一种有前途的季节预测方法。