On the state-dependent predictability horizon in dynamical and AI forecasts

Forecast skill on subseasonal-to-seasonal timescales varies strongly with the large-scale atmospheric state, creating intermittent “windows of opportunity” for skillful prediction. Here we evaluate state-dependent predictability of 2m temperature in subseasonal hindcasts with CESM and in a perfect modelling framework. Skill is quantified as a function of the Pacific-North American pattern, the phase of the El Nino Southern Oscillation, the Madden-Julian Oscillation, the North Atlantic Oscillation and the soil state. Both models exhibit regionally significantly enhanced subseasonal skill during dynamically organized flow regimes like the PNA, El Niño or La Nina, and certain MJO phases, when tropical forcing projects onto an amplified winter jet and supports coherent Rossby wave propagation. The resulting predictability is modulated by the seasonality of the background flow. Our findings demonstrate that regional S2S forecast skill may be higher than suggested by spatial averages. It is investigated if AI generated forecasts can capture this state-dependent predictability.