Evaluation and projection of precipitation extremes under 1.5°C and 2.0°C GWLs over China using bias-corrected CMIP6 models
Bibliographic record
Abstract
China is facing an increasing challenge from severe precipitation-related extremes with accelerating global warming. In this study, using a bias-corrected CMIP6 ensemble, future responses of precipitation extreme indices at 1.5°C and 2.0°C global warming levels (GWLs) under the SSP245, SSP370 and SSP585 scenarios are investigated. Despite different change magnitudes, extreme precipitation events will be more frequent and more intense over China as a whole under higher emissions and GWLs. The increase in annual total precipitation could attribute to a sharp increase in the intensity and days of very heavy precipitation in future global warming scenarios. Limiting global warming to 1.5°C and low emission pathways (i.e., SSP245) instead of 2°C and high emission pathways (i.e., SSP585) would have substantial benefits for China in terms of reducing occurrences of extreme precipitation 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".