The spatio-temporal relations of impacts of extreme events and their drivers in climate data are not fully understood and there is a need of machine learning approaches to identify such spatio-temporal relations from data. The task, however, is very challenging since there are time delays between extremes and their drivers, and the spatial response of such drivers is inhomogeneous. In this work, we propose a first approach and benchmarks to tackle this challenge. Our approach is trained end-to-end to predict spatio-temporally extremes and spatio-temporally drivers in the physical input variables jointly. By enforcing the network to predict extremes from spatio-temporal binary masks of identified drivers, the network successfully identifies drivers that are correlated with extremes. We evaluate our approach on three newly created synthetic benchmarks, where two of them are based on remote sensing or reanalysis climate data, and on two real-world reanalysis datasets. The source code and datasets are publicly available at the project page https://hakamshams.github.io/IDE.

本研究解决了极端事件及其气候驱动因素之间时空关系不明确的问题，提出了一种新的机器学习方法来识别这些关系。通过端到端训练模型共同预测极端事件及其驱动因素，研究成功识别了与极端事件相关的驱动因素，评估结果在多个基准数据集上显示了良好的性能，具有重要的应用潜力。

识别极端事件的时空驱动因素