Changes of Extreme Precipitation in CMIP6 Projections: Should We Use Stationary or Nonstationary Models?
Bibliographic record
Abstract
Abstract With global warming, the behavior of extreme precipitation shifts toward nonstationarity. Here, we analyze the annual maxima of daily precipitation (AMP) all over the globe using projections of the latest phase of the Coupled Model Intercomparison Project (CMIP6) under four shared socioeconomic pathways (SSPs). The projections were bias corrected using a semiparametric quantile mapping, a novel technique extended to extreme precipitation. This analysis 1) explores the variability of future AMP globally and 2) investigates the performance of stationary and nonstationary models in describing future AMP with trends. The results show that global warming potentially intensifies AMP. For the nonparametric analysis, the 33-yr precipitation levels are increasing up to 33.2 mm compared to the historical period. The parametric analysis shows that the return period of 100-yr historical events will decrease approximately to 50 and 70 years in the Northern and Southern Hemispheres, respectively. Under the highest emission scenario, the projected 100-yr levels are expected to increase by 7.5%–21% over the historical levels. Using stationary models to estimate the 100-yr return level for AMP projections with trends leads to an underestimation of 3.4% on average. Extensive Monte Carlo experiments are implemented to explain this underestimation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".