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<p>The early warning of heatwaves using seasonal forecasting systems has the potential to mitigate economic losses and risk to life. Because of the limited reliability and computational expense of dynamical forecasting systems, efforts in recent years have turned to exploiting Machine Learning. Here, an inexpensive approach to forecasting summer heatwaves over Europe is developed, using an optimisation-based feature selection framework to detect a combination of variables, domains and time-lags used to skilfully predict heatwaves. The purely data-driven forecasts are shown to match, and in places outperform, the skill of the state-of-the-art dynamical multi-model products. Moreover, low skill over Scandinavia, a long-term issue common to most dynamical systems, is improved in our data-driven approach. This work also highlights that the greatest contribution to skill comes from predictors at 4-7 weeks time-lag (e.g. mid-March), and identifies predictors which can form the basis for future studies on mechanisms.</p>

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Feature selection for data-driven seasonal forecasts of European heatwaves

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月4日

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  • Ronan McAdam,
  • Jorge Pérez-Aracil,
  • Antonello Squintu,
  • César Peláez-Rodríguez,
  • Felicitas Hansen,
  • Verónica Torralba,
  • Harilaos Loukos,
  • Eduardo Zorita,
  • Matteo Giuliani,
  • Leone Cavicchia,
  • Sancho Salcedo-Sanz,
  • Enrico Scoccimarro

摘要

The early warning of heatwaves using seasonal forecasting systems has the potential to mitigate economic losses and risk to life. Because of the limited reliability and computational expense of dynamical forecasting systems, efforts in recent years have turned to exploiting Machine Learning. Here, an inexpensive approach to forecasting summer heatwaves over Europe is developed, using an optimisation-based feature selection framework to detect a combination of variables, domains and time-lags used to skilfully predict heatwaves. The purely data-driven forecasts are shown to match, and in places outperform, the skill of the state-of-the-art dynamical multi-model products. Moreover, low skill over Scandinavia, a long-term issue common to most dynamical systems, is improved in our data-driven approach. This work also highlights that the greatest contribution to skill comes from predictors at 4-7 weeks time-lag (e.g. mid-March), and identifies predictors which can form the basis for future studies on mechanisms.