The Response of Daily and Sub‐Daily Extreme Precipitations to Changes in Surface and Dew‐Point Temperatures
Bibliographic record
Abstract
Abstract Extreme precipitation events are expected to increase in frequency and intensity in the future climate, but the magnitude of these changes remains uncertain. The relationship between extreme precipitation and the surface temperature has been investigated to more robustly assess projected increases in extreme precipitation considering that projected temperature is more adequately simulated. Relationships between extreme precipitation (daily and sub‐daily) and surface air temperature (SAT) or surface dew‐point temperature (SDPT) are analyzed in this study using the 50‐members ensemble from the fifth version of the Canadian Regional Climate Model covering the Northeastern North America region over the period 1956–2099. Temperature‐precipitation scaling rates (TPSRs) were estimated using local SAT and SDPT seasonal anomalies as covariate over both periods for 2–100‐year extreme precipitation events and durations ranging from 1 to 24 h. Contrasting responses were obtained when using SAT or SDPT, especially in the southern part of the domain. Median scaling rates over the entire domain for SDPT were close to the Clausius‐Clapeyron scaling ( °C) while they were much smaller for SAT and even negative in southern regions, showing that moisture availability is a key factor for these regions. TPSR based on SDPT is also more robustly constrained and can be used to estimate changes in short‐duration extreme precipitation in a future period from TPSR in the historical period over a large part of the domain.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".