Generative artificial intelligence improves projections of climate extremes
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908588652363776 |
|---|---|
| author | Tie, Ruian Zhong, Xiaohui Shi, Zhengyu Li, Hao Chen, Bin Liu, Jun Libo, Wu |
| author_facet | Tie, Ruian Zhong, Xiaohui Shi, Zhengyu Li, Hao Chen, Bin Liu, Jun Libo, Wu |
| contents | Climate change is amplifying extreme events, posing escalating risks to biodiversity, human health, and food security. GCMs are essential for projecting future climate, yet their coarse resolution and high computational costs constrain their ability to represent extremes. Here, we introduce FuXi-CMIPAlign, a generative deep learning framework for downscaling CMIP outputs. The model integrates Flow Matching for generative modeling with domain adaptation via MMD loss to align feature distributions between training data and inference data, thereby mitigating input discrepancies and improving accuracy, stability, and generalization across emission scenarios. FuXi-CMIPAlign performs spatial, temporal, and multivariate downscaling, enabling more realistic simulation of compound extremes such as TCs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_16396 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Generative artificial intelligence improves projections of climate extremes Tie, Ruian Zhong, Xiaohui Shi, Zhengyu Li, Hao Chen, Bin Liu, Jun Libo, Wu Atmospheric and Oceanic Physics Artificial Intelligence Climate change is amplifying extreme events, posing escalating risks to biodiversity, human health, and food security. GCMs are essential for projecting future climate, yet their coarse resolution and high computational costs constrain their ability to represent extremes. Here, we introduce FuXi-CMIPAlign, a generative deep learning framework for downscaling CMIP outputs. The model integrates Flow Matching for generative modeling with domain adaptation via MMD loss to align feature distributions between training data and inference data, thereby mitigating input discrepancies and improving accuracy, stability, and generalization across emission scenarios. FuXi-CMIPAlign performs spatial, temporal, and multivariate downscaling, enabling more realistic simulation of compound extremes such as TCs. |
| title | Generative artificial intelligence improves projections of climate extremes |
| topic | Atmospheric and Oceanic Physics Artificial Intelligence |
| url | https://arxiv.org/abs/2508.16396 |