Subseasonal and seasonal windows of forecast opportunity of extreme European winter weather

At subseasonal to seasonal lead times, the forecast skill of extreme events is known to be intermittent and dependent on specific phenomena or conditions, such as a strong El Niño event or sudden stratospheric warming. These states of enhanced predictability in the climate system are termed windows of forecast opportunity. Although this concept is widely recognised, diagnosing windows of opportunity remains an issue and often relies on evaluating conditional model skill, thereby conflating the window of opportunity with the ability of the model to represent it. Furthermore, identifying suitable representations of the dynamical drivers that provide enhanced predictability of a specific extreme event remains a challenge. Here, we propose an information-theoretic diagnostic of windows of forecast opportunity, which can be evaluated in a causal inference framework based on reanalysis data. We apply this diagnostic to characterise the seasonal modulation of subseasonal teleconnections relevant to weather extremes over Europe. Furthermore, we demonstrate the ability of a novel targeted clustering approach based on variational autoencoders to identify circulation regimes that disentangle the drivers of a specific extreme while maintaining their own predictability and physical teleconnections at S2S lead times. Combining the novel diagnostic with the improved representation of dynamical drivers provides a way forward to addressing the challenge of identifying windows of opportunity at subseasonal to seasonal lead times.