AI-informed model-analogs for understanding subseasonal-to-seasonal jet stream and North American temperature predictability

ArXi:2506.14022v3 Announce Type: replace-cross Subseasonal-to-seasonal forecasting is crucial for public health, disaster preparedness, and agriculture, and yet it remains a particularly challenging timescale to predict. We explore the use of an interpretable AI-informed model analog forecasting approach, previously employed on longer timescales, to improve S2S predictions.