The authors use conditional GANs to generate future scenarios of drought conditions (measured by a Soil Wetness Index) in France up to 2050. Their model, SwiGAN, produces realistic spatial maps of how drought evolves over time, helping insurance companies plan for climate-driven risks beyond the usual one-year horizon. Drought accounts for 30% of natural catastrophe insurance payouts in France, making long-term forecasting valuable.
Main takeaways:
- Conditional GANs generate plausible future spatial maps of soil wetness under climate change
- Focused on France, where drought is a major insurance expense
- Helps insurers design strategies for climate risk beyond short-term regulations
- The approach is generalizable to other climate perils and actuarial problems
- Trained on historical soil wetness data to learn realistic drought propagation patterns