Forecasting monthly numbers of hot days in Europe with a convolutional neural network

Hot days, defined here as days exceeding the local 90th temperature percentile in summer months, pose an increasing threat to societies as summers warm along the climate. Therefore, an early warning of hot days and heat waves would be beneficial. To alleviate this need, we fit a convolutional neural network model to the global spatial distributions of the ERA5 reanalysis data to forecast the future number of hot days over the nearest 30-day period in Europe. 

A large set of potential input variable candidates were explored, including variables from the stratosphere and from the surface layers. Three-fold cross-validation was used to find the optimal subset to be used in forecasting. In addition to the input variables themselves, we use their temporal differences as predictors. Stepwise backward increasing of the amount of fitting data was applied to study the sensitivity of modelling to the number of fitting years. Finally, to emulate the real forecasting, time series hindcasting was applied by fitting a new model for each forecasted year, using only years prior to each year for fitting.

The target variable – the number of hot days during the nearest month – is extremely season-dependent. The non-linear forecasting model can take this into account, and both the grid cell based numbers of hot days and especially the mean numbers inside sub-regions show that the model is capable of reproducing the numbers. The skill, measured by the anomaly correlation coefficient, increases rapidly and constantly with an increasing number of fitting years. Interestingly, the skill curve does not level out, implying the model could still be enhanced by further increasing the fitting data.