Feature selection for data-driven seasonal forecasts of European heatwaves

The early-warning of heatwaves using seasonal forecasting systems has the potential to mitigate economic losses and loss of life. Because of the limited reliability and computational expense of dynamical forecast systems, efforts in recent years have turned to exploiting the power of Machine Learning. Recent years have seen data-driven methods of forecasting deliver added-value for short-term forecasting, yet work on the seasonal scale is not yet as mature. Within the framework of the European Horizon project “CLINT - Climate Intelligence”, a purely data-driven approach to forecasting summer heatwaves on seasonal timescales has been developed. This approach is based on a novel optimisation-based feature selection framework that detects the optimal combination of variables, domains and lag times used to predict heatwaves. The feature selection is performed on multi-millennial paleo-simulation, ensuring sufficient training data, and it is demonstrated that predictors in the model-world are relevant to predictions of the recent past (1993-2016). For forecasts of summer heatwave propensity initialised in May, the data-driven approach matches the skill of the state-of-the-art dynamical multi-model product over Europe, and even outperforms individual systems, at a considerably lower cost. Moreover, low skill over Scandinavia and northern Europe, a long-term issue common to most dynamical systems, is improved in the data-driven approach. Besides forecasts, the data-driven approach also provides insight into the key predictors of European summer heatwave tendency; in particular most-commonly selected predictors correspond to 1-2 months prior to the start of summer (i.e., March) and some have not yet been discussed in existing literature.