Improved Correction of Extreme Precipitation Through Explicit and Continuous Nonstationarity Treatment and the Metastatistical Approach
Bibliographic record
Abstract
Abstract Climate models simulate extreme precipitation under nonstationarity due to continuous climate change. However, systematic errors in local‐scale climate projections are often corrected using stationary or quasi‐stationary methods without explicit and continuous nonstationarity treatment, like quantile mapping (QM), detrended QM, and quantile delta mapping. To bridge this gap, we introduce nonstationary QM (NS‐QM) and its simplified version for consistent nonstationarity patterns (CNS‐QM). Besides, correction approaches for extremes often rely on limited extreme‐event records. To leverage ordinary‐event information while focusing on extremes, we propose integrating the simplified Metastatistical extreme value (SMEV) distribution into NS‐QM and CNS‐QM (NS‐QM‐SMEV and CNS‐QM‐SMEV). We demonstrate the superiority of NS‐ and CNS‐QM‐SMEV over existing methods through a simulation study and show several real‐world applications using high‐resolution‐regional and coarse‐resolution‐global climate models. NS‐QM and CNS‐QM reflect nonstationarity more realistically but may encounter challenges due to data limitations like estimation errors and uncertainty, particularly for the most extreme events. These issues, shared by existing approaches, are effectively mitigated using the SMEV distribution. NS‐ and CNS‐QM‐SMEV offer lower estimation error, approximate unbiasedness, reduced uncertainty, and improved representation of the entire distribution, especially for samples of ∼70 years, and greater superiority with larger samples. We show existing methods may perform competitively for short samples but exhibit substantial biases in quantile‐quantile matching due to bypassing nonstationarity modeling. NS‐ and CNS‐QM‐SMEV avoid these biases, adhering better to their theoretical functioning. Thus, NS‐ and CNS‐QM‐SMEV enhance the correction of extremes under nonstationarity. Yet, properly identifying nonstationarity patterns is crucial for reliable implementations.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".