We developed a generative deep learning method combining generative adversarial networks and extreme value theory to simulate spatially resolved multi-hazards events across large regions. Tested on storms in the Bay of Bengal, the model captured spatial patterns of wind, pressure, and rainfall during storm events, enabling more realistic disaster risk assessments than traditional methods. This flexible framework can be applied to various hazards and regions for improved disaster planning.