Application of Artificial Intelligence in Subseasonal to Seasonal Prediction: Progress and Prospects

Subseasonal-to-seasonal (S2S) prediction on the 2–8-week timescale plays a vital role in food security, water management, and disaster risk reduction. By learning nonlinear relationships directly from observational and reanalysis data, artificial intelligence (AI) has emerged as an important technical pathway for advancing S2S prediction. This paper reviews recent progress along two complementary directions. Machine-learning post-processing of dynamical outputs effectively corrects forecast biases in temperature and precipitation. Data-driven models, in parallel, have evolved from target-specific systems to global multi-variable architectures, and further to multi-sphere fully coupled Earth system models, showing particularly strong skill for large-scale circulation such as the Madden–Julian Oscillation (MJO). Beneath this progress, however, forecast skill across current architectures deteriorates sharply beyond 4–5 weeks, and AI models systematically underestimate record-breaking extremes. Breaking this ceiling will require multi-source data fusion to recover tail information, probabilistic objective functions optimized with embedded physical constraints to preserve extreme signals, and interpretable architectures to support operational trustworthiness—ultimately moving toward observation-driven, end-to-end Earth system intelligent prediction with multi-sphere coupling.