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Climate hazards can cause major disasters when they occur simultaneously ascompound hazards. To understand the distribution of climate risk and informadaptation policies, scientists need to simulate a large number of physicallyrealistic and spatially coherent events. Current methods are limited bycomputational constraints and the probabilistic spatial distribution ofcompound events is not given sufficient attention. The bottleneck in currentapproaches lies in modelling the dependence structure between variables, asinference on parametric models suffers from the curse of dimensionality.Generative adversarial networks (GANs) are well-suited to such a problem due totheir ability to implicitly learn the distribution of data in high-dimensionalsettings. We employ a GAN to model the dependence structure for daily maximumwind speed, significant wave height, and total precipitation over the Bay ofBengal, combining this with traditional extreme value theory for controlledextrapolation of the tails. Once trained, the model can be used to efficientlygenerate thousands of realistic compound hazard events, which can informclimate risk assessments for climate adaptation and disaster preparedness. Themethod developed is flexible and transferable to other multivariate and spatialclimate datasets.

Combining deep generative models with extreme value theory for synthetic hazard simulation: a multivariate and spatially coherent approach