Rarest rainfall events have greatest relative increase under climate change
Bibliographic record
Abstract
Future rainfall extremes are expected to increase due to global warming based on both theoretical considerations and climate model outcomes. Common (yearly) extremes and rare (decennial or centennial) extremes may be affected differently. Here we show that the rarer the event, the more it is relatively expected to increase in a future climate. For the mitigation scenario SSP1-2.6 and the high emission scenario SSP5-8.5 daily land rainfall extremes will increase by 10.5 % and 28.2 %, respectively, for yearly events and by 13.5 % and 38.3, respectively, for 100-year events by the end of this century. These numbers are based on frequency and extreme value analyses applied to 25 different CMIP6 earth system models, weighted for independence and performance. The findings are consistent and statistically significant across all 25 earth system models, 102 different model runs, and four different possible climate futures. Our results show distinct regional differences, with some regions disproportionally affected relative to others. This has important implications for engineering design standards, which need to be raised more for systems designed for the rarest events.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".