Observations Over a Century Underscore an Increasing Likelihood of Compound Dry‐Hot Events in China
Bibliographic record
Abstract
Abstract The impacts of extreme events are seldom caused by a single climatic variable but rather arise from the interaction of multiple climate drivers. This study employs observational data sets with high spatiotemporal resolution to analyze the risk of occurrence of compound dry‐hot events in China over the past 120 years (i.e., 1901–2020). Simultaneously, attribution analysis based on distribution functions explores whether and to what extent human activities influence the occurrence of compound events. The results indicate that over the historical 120‐year period, the frequency of compound dry‐hot events in China has gradually increased, with the highest frequency observed in the most recent 40 years (i.e., 1981–2020). The frequency of compound dry‐hot events during this period is approximately four times that of 1901–1940 and about twice that of 1941–1980. The analysis of the relative importance of different factors reveals that temperature changes contribute more (56%) to the occurrence of compound events than precipitation (23%), and also exceed the interaction between them (21%). The substantial increase in compound dry‐hot events is largely attributed to the influence of human activities. Across seven sub‐regions, human activities have led to an increase in the probability of compound events occurring, ranging from 7.9% to 31.6%. The findings of this study indicate that human activities have significant implications for explaining the observed increase in compound hot and dry events over the past 40 years.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".