Spatiotemporal Heterogeneity in Precipitation over China and Its Connections with Large-Scale Climate Oscillations—A Moisture Budget Perspective
Bibliographic record
Abstract
Abstract Climate change can lead to variations in the probability distribution of precipitation. In this study, quantile regression (QR) is undertaken to identify the quantile trends in precipitation over China and to examine the quantile effects of various climate oscillations on precipitation. The results show that the quantile trends show apparent seasonal variations, with a greater number of stations showing trends in winter (especially at quantile levels ≥ 0.5), and larger average magnitudes of trends at nearly all quantile levels in summer. The effects of El Niño–Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), and Pacific decadal oscillation (PDO) exhibit evident variations with respect to the quantile level. Spatial clusters are subsequently identified based on the quantile trends, and the individual and combined effects from the teleconnection patterns are further investigated from the perspective of moisture budget. Seven spatial clusters with distinct seasonal quantile trends can be identified; three of them are located in southeastern China and are characterized by increasing trends in summer and winter precipitation. Summer precipitation over this region is positively influenced by ENSO and negatively influenced by NAO, with the former affecting both the dynamic and thermodynamic components of vertically integrated moisture divergence and the latter affecting only the dynamic component. The interaction effect of ENSO and NAO on summer precipitation anomalies in months that are extremely wetter than normal is statistically significant. In comparison, winter precipitation in this region is under the positive influence of ENSO and NAO and the negative influence of PDO; the effect of ENSO on moisture convergence can be mainly attributed to its dynamic component.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 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 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".