Projected changes in heat, extreme precipitation, and their spatially compound events over China’s coastal lands and seas through a high-resolution climate models ensemble
Bibliographic record
Abstract
Abstract China’s coastal lands and seas are highly susceptible to the changing environment due to their dense population and frequent economic activities. These areas experience more significant impacts from climate change-induced extreme events than elsewhere. The most noticeable effects of climate change are extreme high temperatures and extreme precipitation. We employ an ensemble of RCMs (Regional Climate Models) to investigate and project changes in temperature, precipitation, and Compound Heat-Precipitation Extreme events (CHPEs) over selected China’s coastal lands and seas for both historical (1985–2004) and future periods (2080–2099). The multi-model ensemble projects that daily temperature extremes will increase by 2.9 °C to 5.4 °C across China’s coastal lands and seas, with land areas showing a higher temperature increase than marine areas. Extreme precipitation shows a high geographical heterogeneity with a 2.8–3.9 mm d−1 reduction over the 15–25°N marine areas while a 2.2–5.4 mm d−1 increment over the 25°N-35°N land areas. We use the Clausius–Clapeyron relationship to reveal that the peak of daily extreme precipitation will increase by 2–7 mm d−1 and the temperature at which extreme precipitation peaks will increase by 2 °C to 6 °C by warming. The land area of 25–30°N has the highest peak precipitation increase of 9.87 mm d−1 and a peak temperature increase of 6 °C. As precipitation extremes intensify with daily temperature extremes increase, CHPEs are projected to occur more frequently over both land and marine areas. Compared with the historical period, the frequency of CHPEs will increase by 40.9%-161.2% over marine areas, and by 36.2%-163.6% over land areas in the future. The 15–20°N area has the highest frequency increase of CHPE events, and the 25–30°N area has the largest difference in frequency increase under two different scenarios. It indicated that the 25–30°N area will be more easily affected by climate change.
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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) | 0.001 | 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".