Refine Extreme Hot Day Predictions with the Sea Surface Temperature Tendency

The extreme high temperature in western North America (WNA) exerts profound impacts on industrial and agricultural production, and trigger catastrophic wildfires. Exploring the underlying mechanisms influencing extreme hot days over WNA (WEHDs) and improving the seasonal prediction are of great scientific and social significance. This study reveals that two independent precursor signals, the persistent negative sea surface temperature (SST) anomalies in tropical eastern Pacific and the cooling tendency in tropical North Atlantic SST during springtime exhibit significant influence on WEHDs. A physics-based empirical model constructed using these two predictors exhibits robust independent prediction skills. Guided by the underlying physical mechanisms, we integrate SST tendency fields as critical input features into convolutional neural network (CNN) to further enhance the prediction accuracy. The physically informed CNN achieves significantly improved performance and successfully predicts the extreme WEHD events of 2021. The results emphasize the pivotal role of physical cognition in advancing deep learning-based climate prediction.