Data-driven seasonal forecasts of European heat waves

Seasonal Forecasts are critical tools for early-warning decision support systems, that can help reduce the related risk associated with hot or cold weather and other events that can strongly affect a multitude of socio-economic sectors. Recent advances in both statistical approaches and numerical modeling have improved the skill of Seasonal Forecasts. However, especially in mid-latitudes, they are still affected by large uncertainties that can limit their usefulness.

The MSCA-H2020 project ARTIST aims at improving our knowledge of climate predictability at the seasonal time-scale, focusing on the role of unexplored drivers, to finally enhance the performance of current prediction systems. This effort is meant to reduce uncertainties and make forecasts efficiently usable by regional meteorological services and private bodies. This study focuses on seasonal prediction of heat extremes in Europe, and here we present a first attempt to predict heat wave accumulated activity across different target seasons. An empirical seasonal forecast is designed based on Machine Learning techniques. A feature selection approach is used to detect the best subset of predictors among a variety of candidates, and then an assessment of the relative importance of each predictor is done, in different European regions for the four main seasons.

Results show that many observed teleconnections are caught by the data-driven approach, while a few features that show to be linked to the heat wave propensity of a season deserve a deeper understanding of the underpinning physical process.