A Wasserstein GAN-based climate scenario generator for risk management and insurance: the case of soil subsidence
Authors: Antoine Heranval (BioSP), Olivier Lopez (CREST), Didier Ngatcha (CREST) et al.
Summary
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
Relevance
Not related to my persona or midtraining work — this is about climate modeling and insurance risk, not language model behavior or alignment.
Abstract
arXiv:2605.06678v1 Announce Type: new Abstract: According to the United Nations Office for Disaster Risk Reduction (2025), the average annual cost of natural catastrophes increased from 70--80 billion USD between 1970 and 2000 to 180--200 billion USD between 2001 and 2020. Reports from organizations such as the IFOA and the WWF highlight the need for the insurance sector to adapt to this rapidly evolving context by developing medium- to long-term strategies that go beyond the one-year horizon of prudential regulations such as Solvency II. This paper introduces an artificial intelligence framework based on Conditional Generative Adversarial Networks (Conditional GANs) to generate future spatio-temporal trajectories of climatic indices. The approach focuses on the Soil Wetness Index (SWI), a key indicator used in France to assess drought severity. Drought accounts for approximately 30% of the indemnities paid under the French natural catastrophe insurance scheme. The proposed model, SwiGAN, simulates plausible drought propagation patterns up to 2050 for a region of France particularly exposed to this hazard. By generating realistic sequences of SWI maps, SwiGAN provides insights into drought dynamics under climate change scenarios and supports the design of adaptive risk management and insurance strategies. The methodology is also generalizable to other climate-related perils and actuarial applications such as economic scenario generation.