Probabilistic forecasts of extreme heatwaves using convolutional neural networks in a regime of lack of data

Understanding extreme events and their probability is key for the study of climate change impacts, risk assessment, adaptation, and the protection of living beings. In this work we develop a methodology to build forecasting models for extreme heatwaves. These models are based on convolutional neural networks, trained on extremely long 8,000-year climate model outputs. Because the relation between extreme events is intrinsically probabilistic, we emphasise probabilistic forecast and validation. We demonstrate that deep neural networks are suitable for this purpose for long lasting 14-day heatwaves over France, up to 15 days ahead of time for fast dynamical drivers (500 hPa geopotential height fields), and also at much longer lead times for slow physical drivers (soil moisture). The method is easily implemented and versatile. We find that the deep neural network selects extreme heatwaves associated with a North-Hemisphere wavenumber-3 pattern. We find that the 2 meter temperature field does not contain any new useful statistical information for heatwave forecast, when added to the 500 hPa geopotential height and soil moisture fields. The main scientific message is that training deep neural networks for predicting extreme heatwaves occurs in a regime of drastic lack of data. We suggest that this is likely the case for most other applications to large scale atmosphere and climate phenomena. We discuss perspectives for dealing with the lack of data regime, for instance rare event simulations, and how transfer learning may play a role in this latter task.