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Reliable sub-seasonal forecasts of high summer temperatures would be very valuable for society. Although state-of-the-art numerical weather prediction (NWP) models have become much better in representing the relevant sources of predictability like land- and sea-surface states, the sub-seasonal potential is not fully realized. Complexities arise because drivers depend on the state of other drivers and on interactions over multiple time-scales. This study applies statistical modeling to ERA5 reanalysis data, and explores how nine potential drivers, interacting on eight time-scales, contribute to the sub-seasonal predictability of high summer temperatures in western and central Europe. Features and target temperatures are extracted with two variations of hierarchical clustering, and are fitted with a machine learning (ML) model based on Random Forests. Explainable AI methods show that the ML model agrees with physical understanding. Verification of the forecasts reveals that a large part of predictability comes from climate change, but that reliable and valuable sub-seasonal forecasts are possible in certain windows, like forecasting monthly warm anomalies with a lead time of 15 days. Contributions of each driver confirm that there is a transfer of predictability from the land- and sea-surface state to the atmosphere. The involved time-scales depend on lead time and the forecast target. The explainable AI methods also reveal surprising driving features in sea-surface temperature and 850 hPa temperature, and rank the contribution of snow-cover above that of sea-ice. Overall, this study demonstrates that complex statistical models, when made explainable, can complement research with NWP models, by diagnosing drivers that need further understanding and a correct numerical representation, for better future forecasts.

Using Explainable Machine Learning Forecasts to Discover Subseasonal Drivers of High Summer Temperatures in Western and Central Europe