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.

TC is characterised by warm core, and a low and availability of huge data, new models using Artificial pressure system with a large vortex in the atmosphere. TC Neural Networks (ANNs) have been increasingly used to brings strong winds, heavy precipitation and high tides in forecast track and intensity of cyclones (Leroux et al. 2018; coastal areas and resulted in huge economic and human loss. Alemany et al. 2018; Giffard-Roisin et al. 2020; Moradi Kordmahalleh, Over the years, many destructive TCs have originated in the Gorji Sefidmazgi, and Homaifar 2016). North Indian Ocean (NIO), consisting of the Bay of Bengal The most important prediction about a TC is its arrival at and the Arabian Sea. In 2008, Nargis, one of the disastrous land, known as landfall of a cyclone. The accurate prediction TC in recent times, originated in the Bay of Bengal and resulted about the location and time of the landfall, and intensity of in 13,800 casualties alone in Myanmar and caused the cyclone at the landfall will hugely help authorities to take US$15.4 billion economic loss (Fritz et al. 2009). In 2018, preventive measures and reduce material and human loss. In Fani cyclone caused 89 causalities in India and Bangladesh, this work, we attempt to predict intensity, location, and time and US$9.1 billion economic loss (Kumar, Lal, and Kumar of the landfall of a TC at any instance of time during the 2020).

The seasonal monsoon, which hits the region between June and September, delivers some 70 percent of India's surface water. Its arrival is eagerly awaited by hundreds of millions of subsistence farmers across the country, and delays can ruin crops or exacerbate drought.