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We present a data-driven emulator, stochastic weather generator (SWG),suitable for estimating probabilities of prolonged heatwaves in France andScandinavia. This emulator is based on the method of analogs of circulation towhich we add temperature and soil moisture as predictor fields. We train theemulator on an intermediate complexity climate model run and show that it iscapable of predicting conditional probabilities (forecasting) of heatwaves outof sample. Special attention is payed that this prediction is evaluated usingproper score appropriate for rare events. To accelerate the computation ofanalogs dimensionality reduction techniques are applied and the performance isevaluated. The probabilistic prediction achieved with SWG is compared with theone achieved with  Convolutional Neural Network (CNN). With the availability of hundreds ofyears of training data CNNs perform better at the task of probabilisticprediction. In addition, we show that the SWG emulator trained on 80 years ofdata is capable of estimating extreme return times of order of thousands ofyears for heatwaves longer than several days more precisely than the fit basedon generalised extreme value distribution. Finally, the quality of itssynthetic extreme teleconnection patterns obtained with stochastic weathergenerator is studied. We showcase two examples of such synthetic teleconnectionpatterns for heatwaves in France and Scandinavia that compare favorably to thevery long climate model control run.

Extreme heatwave sampling and prediction with analog Markov chain and comparisons with deep learning