ABSTRACT
Climate change poses significant challenges to global ecosystems, economies, and human societies, necessitating advanced forecasting and impact assessment methodologies. Traditional climate models, while effective, often struggle with high computational costs and limited resolution. This paper explores the application of advanced generative modeling, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and diffusion models, for improving climate forecasting and impact assessment. These models enhance spatial and temporal resolution, generate synthetic climate scenarios, and improve uncertainty quantification. Despite challenges such as data limitations, computational demands, and model interpretability, generative modeling provides a powerful tool for more accurate, scalable, and data-driven climate projections. Future research should focus on integrating multimodal datasets, improving model explainability, and ensuring ethical deployment. This study highlights the transformative potential of AI-driven climate modeling in shaping adaptive and resilient climate policies.
Keywords:Generative Modeling, Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), AI-Driven Climate Modeling, Multimodal Datasets, Model Interpretability, Climate Policies, Adaptive Climate Policies.


OBJECTIVE
1. To develop advanced forecasting models using Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) for accurate prediction of climate-related events such as temperature, rainfall, and sea-level rise.
2. To enhance the spatial and temporal resolution of climate projections through generative modeling techniques.
3. To assess the impacts of climate change on various sectors, including agriculture, ecosystems, and human health, using regression models, classification, and clustering methods.
4. To generate synthetic climate data that reflects historical patterns, improving model training and enabling robust climate scenario simulations.
5. To evaluate the performance of the proposed models using standard metrics and compare them with traditional climate forecasting methods.