Climate hazards can cause major disasters when they occur simultaneously as compound hazards. To understand the distribution of climate risk and inform adaptation policies, scientists need to simulate a large number of physically realistic and spatially coherent events. Current methods are limited by computational constraints and the probabilistic spatial distribution of compound events is not given sufficient attention. The bottleneck in current approaches lies in modelling the dependence structure between variables, as inference on parametric models suffers from the curse of dimensionality. Generative adversarial networks (GANs) are well-suited to such a problem due to their ability to implicitly learn the distribution of data in high-dimensional settings. We employ a GAN to model the dependence structure for daily maximum wind speed, significant wave height, and total precipitation over the Bay of Bengal, combining this with traditional extreme value theory for controlled extrapolation of the tails. Once trained, the model can be used to efficiently generate thousands of realistic compound hazard events, which can inform climate risk assessments for climate adaptation and disaster preparedness. The method developed is flexible and transferable to other multivariate and spatial climate datasets.

翻译：气候灾害在作为复合灾害同时发生时，可能引发重大灾难。为理解气候风险分布并制定适应政策，科学家需要模拟大量物理真实且空间连贯的事件。现有方法受计算条件限制，且对复合事件概率空间分布的关注不足。当前方法的主要瓶颈在于变量间依赖结构的建模，因为参数模型的推断面临维度灾难问题。生成对抗网络（GAN）因能隐式学习高维数据分布的特性，非常适合解决此类问题。我们采用GAN对孟加拉湾每日最大风速、有效波高与总降水量之间的依赖结构进行建模，并结合传统极值理论对尾部进行可控外推。训练完成后，该模型可高效生成数千个真实的复合灾害事件，为气候适应与灾害防备的气候风险评估提供依据。该方法具有灵活性和可迁移性，适用于其他多变量空间气候数据集。