【主讲人简介】：王中雷，博士生导师，厦门大学王亚南经济研究院副教授。王中雷致力于复杂抽样调查样本的统计推断理论研究以及统计方法在气象水文领域的交叉应用研究，并取得部分原创性成果。

【内容简介】：El Niño-Southern Oscillation (ENSO) is a prominent mode of interannual climate variability with farreaching global impacts. Its evolution is governed by intricate air-sea interactions, posing significant challenges for long-term prediction. In this study, we introduce CTEFNet, a multivariate deep learning model that synergizes convolutional neural networks and transformers to enhance ENSO forecasting. By integrating multiple oceanic and atmospheric predictors, CTEFNet extends the effective forecast lead time to 20 months while mitigating the impact of the spring predictability barrier, outperforming both dynamical models and state-of-the-art deep learning approaches. Furthermore, CTEFNet offers physically meaningful and statistically significant insights through gradient-based sensitivity analysis, revealing the key precursor signals that govern ENSO dynamics, which align with well-established theories and reveal new insights about inter-basin interactions among the Pacific, Atlantic, and Indian Oceans. The CTEFNet’s superior predictive skill and interpretable sensitivity assessments underscore its potential for advancing climate prediction. Our findings highlight the importance of multivariate coupling in ENSO evolution and demonstrate the promise of deep learning in capturing complex climate dynamics with enhanced interpretability.

【讲座时间】：2026年6月23日（星期二）上午10:00

【讲座地点】：人文社科科研楼1801会议室