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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2026 John Ashcroft et al.A key challenge in flood risk analysis is the construction of hazard events that are physically plausible yet extend beyond historical observations with appropriate frequency and spatial coherence. This is commonly addressed through large simulations of synthetic weather scenarios that sample low-likelihood, high-impact events beyond the observed record. Although popular in industrial risk-based workflows, traditional statistical approaches to synthetic weather generation can be limited in their ability to represent the full range of physically plausible variability and spatial structure. Here, we demonstrate a framework that uses an AI-based weather model as a stochastic generator of event sets suitable for flood risk assessment. We adapt the huge ensembles (HENS) approach using a Spherical Fourier Neural Operator (SFNO)-based atmospheric model combined with a diagnostic precipitation model, forming a framework termed “PrecipHENS”. This framework produces more than 1000 synthetic European winter seasons of precipitation and temperature at 0.25° resolution, with modest computational cost (using NVIDIA Earth-2 stack, 112 GPU hours on NVIDIA L40s GPUs). Using an Elbe River case study, we evaluate PrecipHENS against risk-relevant criteria, including reproduction of present-day climatology, preservation of spatial and temporal dependence, representation of extremes, and extrapolation beyond the historical record in event space. PrecipHENS reproduces key features of precipitation and temperature climatology, preserves spatial dependence, including the decay of extremal co-occurrence with distance, and generates a substantially broader diversity of extreme precipitation events than an industry-standard conditional multivariate extreme-value benchmark. Principal component analysis of extreme precipitation fields shows that PrecipHENS spans a much broader space of storm structures than the benchmark or the historical record, indicating it is able to produce previously unseen weather rather than repetition of past patterns. To assess flood risk relevance, the AI-generated weather sequences are coupled with a hydrological model. The resulting river flow simulations are consistent with observed climatology and extreme discharge behaviour, demonstrating that meteorological realism translates into physically plausible hydrological response. Together, these results show that AI-based large-ensemble weather generation can support event set construction for flood hazard and flood risk applications. More broadly, this framework provides a pathway for expanding the physically plausible sample space in applications that require robust characterisation of extremes, including risk assessment, climate-impact analysis, and storyline development.
Author(s): Ashcroft J, Poulston A, Koch M, Ertl G, Brown K, Butler J, Hammond A, Jordan O, Warren S, Lamb R, Young PJ, Wood D
Publication type: Article
Publication status: Published
Journal: Natural Hazards and Earth System Sciences
Year: 2026
Volume: 26
Issue: 7
Pages: 3129-3161
Online publication date: 07/07/2026
Acceptance date: 14/06/2026
Date deposited: 27/07/2026
ISSN (print): 1561-8633
ISSN (electronic): 1684-9981
Publisher: Copernicus Publications
URL: https://doi.org/10.5194/nhess-26-3129-2026
DOI: 10.5194/nhess-26-3129-2026
Data Access Statement: Due to its proprietary nature and competitive interest, the software code in its entirety cannot be made openly available. The individual AI models and their associated trained weights that form the PrecipHENS workflow (see Fig. 1), along with the HENS workflow, are available under an Apache 2.0 license at https://github.com/NVIDIA/earth2studio (PhysicsNeMo Contributors, 2024). The GR4J hydrological model underlying the river results is available under an MIT license at https://github.com/kratzert/RRMPG (Certík et al., 2022). A subset of the datasets can be shared for academic research purposes upon reasonable request. Interested researchers may contact the lead authors to request access.
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