Probabilistic Forecasting of Climate Extremes Using Extreme Value Theory and Deep Generative Models

Accurate forecasting of climate extremes such as floods, heatwaves, and severe storms is vital for risk mitigation and climate resilience planning. Traditional statistical models often fail to capture the nonlinear dynamics and tail dependencies inherent in such events. This paper proposes a novel hybrid framework that integrates Extreme Value Theory (EVT) with Deep Generative Models (DGMs), specifically Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs), for probabilistic modeling and simulation of rare climate phenomena. EVT is employed to model the marginal distributions of extreme events using the Generalized Pareto and Generalized Extreme Value distributions. Meanwhile, DGMs learn latent representations from high-dimensional climate data and synthesize realistic, tail-aware samples. The proposed model captures both the statistical rigor of EVT and the expressive power of deep learning. Empirical evaluations are conducted using ERA5 reanalysis and satellite datasets, focusing on extreme precipitation and temperature anomalies across diverse regions. Results show that the hybrid EVT–DGM framework significantly improves tail risk estimation, return level prediction, and generative quality compared to conventional models. This approach provides a robust tool for data-driven climate risk forecasting under uncertainty.