Comparison of Continuous and Quantile-Based Downscaling Approaches to Evaluate the Climate Change Impacts on Characteristics of Extreme Rainfall
Bibliographic record
Abstract
Extreme precipitation events can cause flooding leading to severe socioeconomic and environmental impacts. Drawing on General Climate Model (GCMs) projections, an assessment of the potential impacts of climate change on the characteristics of extreme precipitation events was undertaken. Statistical downscaling approaches, along with temporal disaggregation techniques, were implemented to estimate continuous precipitation series at fine spatiotemporal scales, allowing extreme precipitation events to be extracted. Quantile-based downscaling approaches have recently been applied to estimate future extreme precipitation quantiles through empirical relationships between GCM and observed precipitation quantiles. It remains unclear how these two approaches differ from one another in terms of their projections. In this study, two quantile- and one continuous-based approaches were employed to estimate the future extreme precipitation quantiles in Montréal, Canada. These downscaling approaches provided an acceptable performance during the historical period; however, their estimates of the sign and magnitude of future extreme precipitation quantile changes differed considerably.
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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 | 0.002 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".