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

翻译：我们提出一种数据驱动仿真器——随机天气生成器（SWG），适用于估算法国和斯堪的纳维亚地区持续热浪的概率。该仿真器基于环流类比方法，并加入温度和土壤湿度作为预测因子场。我们在中等复杂度气候模式运行数据上训练该仿真器，并证明其能够预测样本外热浪的条件概率（预报）。特别关注使用适用于稀有事件的恰当评分来评估这一预测。为加速类比计算，采用了降维技术并评估了性能。将SWG实现的概率预测与卷积神经网络（CNN）实现的预测进行比较。在拥有数百年训练数据的情况下，CNN在概率预测任务上表现更优。此外，我们表明，基于80年数据训练的SWG仿真器能够比基于广义极值分布的拟合更精确地估算持续数天以上热浪的千年量级极端重现期。最后，研究了通过随机天气生成器获得的合成极端遥相关模式的质量。我们展示了法国和斯堪的纳维亚地区热浪的两个合成遥相关模式实例，这些模式与非常长的气候模式控制运行结果相比具有良好一致性。