Interdecadal Variations of the Temporal and Spatial Distribution of Summer Extreme Heat in China
Bibliographic record
Abstract
This study reveals the spatial and temporal variations of summer extreme heat (EH) distribution in China under the background of anthropogenic climate change. Using data from 1542 meteorological stations in China during the 1961–2018 period, both the interannual and interdecadal variations of the frequency and intensity of EH are analyzed. Variations in the temporal and spatial distribution of EH in summer, which is categorized into early summer and midsummer, are determined through empirical orthogonal function (EOF) analysis. The results show that summer EH in China has the following characteristics in terms of time of occurrence and variation of spatial distribution on both intraseasonal and interdecadal time scales. Since 2000, the range and intensity of EH has increased in May, especially during the 2010s with an obvious early onset. The spatial distribution of EH in summer features an intraseasonal variation. In the Huanghuai region, EH mainly occurs in early summer (June), while in the Jiangnan and Jianghuai regions it occurs in midsummer (July–August). Since 2010, the frequency and intensity of EH has decreased in early summer but increased in conventional summer (June–August) with significant intraseasonal variation. The variation trend of the frequency and intensity of EH in North China, Huanghuai region, and Jianghuai region in both early summer and midsummer are consistent with the trend across the entire country, indicating that these regions are positive contributors to EH variation throughout the country.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 teacher head, 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".