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.

翻译：极端事件的影响与其驱动因子在气候数据中的时空关系尚未被充分理解，亟需机器学习方法从数据中识别此类时空关系。然而，该任务极具挑战性，因为极端事件与其驱动因子之间存在时间延迟，且此类驱动因子的空间响应具有非均匀性。在本工作中，我们提出了应对这一挑战的首个方法与基准测试集。我们的方法采用端到端训练，旨在联合预测极端事件的时空分布以及物理输入变量中的时空驱动因子。通过强制网络从已识别驱动因子的时空二值掩码中预测极端事件，该网络成功识别出与极端事件相关的驱动因子。我们在三个新构建的合成基准测试集（其中两个基于遥感或再分析气候数据）以及两个真实世界再分析数据集上评估了所提方法。源代码与数据集已在项目页面 https://hakamshams.github.io/IDE 公开提供。