Global analysis of extreme precipitation changes in the Köppen-Geiger climate classification
Bibliographic record
Abstract
Changes in the frequency and intensity of extreme precipitation resulting from climate change are responsible for natural disasters such as severe floods and have been a major study focus during the last decades. Previous studies have mainly focused on the trends of annual maxima precipitation at global and regional scales. However, little is known about how extreme precipitation trends change among different climate types. This study offers a global analysis of extreme precipitation changes in terms of climate type by using over 8500 gauge-based records. We focus on the period 1964 to 2013 when global warming was accelerating. A climate type is assigned to each station based on the Köppen Geiger (KG) climate classification, resulting in 30 KG climate subtypes. Mann-Kendall test and Sen’s slope estimator are applied to each time series, measuring the magnitude and significance of trends. The heaviness of the tail for each station is assessed based on the shape parameter of the Generalized Extreme Value distribution. Our results indicate a decreasing trend for the majority of stations associated with some of the arid, temperate, and continental subtypes (i.e., hot semi-arid (BSh); hot-summer temperate (Csa); warm-summer temperate (Csb); and warm, dry-summer continental (Dsb)). An increasing trend is observed for the stations associated with the remaining KG subtypes, especially stations associated with dry-summer subarctic (Dsc) and monsoon-influenced extremely cold subarctic (Dwd). A significant increasing trend is estimated for 9.7% of stations located in the eastern USA, Asia, and northern Europe. However, only 2% of stations, mainly in eastern Australia and the central USA have a significant decreasing trend. The heaviness of the tail is the largest in the Polar major climate type (E), followed by Tropical (A), Dry (B), Continental (D), and Temperate (C). For the climate subtypes, large heavy-tailed extremes are observed in extremely cold subarctic (Dfd), polar tundra (ET), and tropical monsoon (Am), while only light-tailed extremes were observed in subpolar oceanic (Cfc). This study reveals the relationship of extreme precipitation characteristics (e.g., tail heaviness and trend) with the climate types at the global scale.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".