Rainfall Extremes in a Changing Climate: Implications for Flood Risk and (Re)Insurance
Bibliographic record
Abstract
Flooding represents a growing concern for the (re)insurance industry, with precipitation extremes as a key driver of flood risk. Some of the most destructive flood events in 2024 were driven by extreme rainfall occurrences, although with important differences in spatial and temporal scales (e.g. Dubai floods, Ex-Hurricane Debby floods in Canada, Central Europe Floods, Hurricane Helene flooding in Georgia and North Carolina, Valencia floods).Ongoing climate trends introduce additional uncertainty in the estimates of intensity, frequency, and distribution of rainfall extremes, complicating their quantification and risk assessment. Understanding and modelling these extremes is critical for improving flood risk management and financial preparedness.This study investigates rainfall extremes in the United States across various temporal scales, focusing on their role in different types of flood risks. We compare multiple statistical models to estimate extreme precipitation values, including approaches that incorporate climate trends. By analysing spatial and temporal patterns of extremes, we evaluate how well these models capture underlying processes and improve predictive accuracy.Our findings suggest that integrating additional information about climate trends and hydrometeorological processes enhances the accuracy of extreme rainfall estimates, moving in the right direction, although given the rare nature of these extremes looking at historical data alone leaves space for future unexpected outcomes. These results provide valuable insights for improving catastrophe models and stress-testing (re)insurance portfolios.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".