Probabilistic Deep Learning Identifies Windows of Opportunity and Precursors for Western North Pacific Subtropical High Prediction

The Western North Pacific Subtropical High (WNPSH) is one of the dominant subtropical anticyclonic circulations over the western North Pacific during boreal summer, strongly influencing East Asian extremes such as tropical cyclone tracks, heatwaves, and the Baiu/Meiyu front. WNPSH variability reflects both midlatitude teleconnections and tropical intraseasonal oscillations (BSISO). Therefore, to clarify predictability, it is essential to identify and quantify how individual events contribute to forecast skill and uncertainty.

We develop a probabilistic deep learning framework to predict a WNPSH index with explicit uncertainty, represented as Gaussian regression outputs (μ, σ), and assess its predictability up to a 1-month lead. We adopt a model that combines a three-dimensional convolutional neural network with self-attention. To capture diverse representations, we pretrain the model using a millennial-scale ensemble dataset from d4PDF and then fine-tune it with the ERA5 reanalysis. As a result, the prediction skill reaches ACC = 0.6 at 10-day lead time. With deep learning models, the prediction problem can be formulated as an explainable AI (XAI) task, in which precursor signals relevant to the forecast can be estimated directly from spatial patterns and input variables (Maeda and Satoh, 2025). Here, we analyze the predictability using a combination of XAI and the concept of windows of opportunity. During opportunity events, forecast skill improves to about a 15-day lead time. Clear precursor patterns emerge in the initial conditions, including signatures of intraseasonal oscillations and midlatitude wave trains. These signals are consistent with heatmap-based interpretations from XAI, providing quantitative statistics on the sources of predictability for prominent events.