Global and Regional Increase of Precipitation Extremes Under Global Warming
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Global warming is expected to change the regime of extreme precipitation. Physical laws translate increasing atmospheric heat into increasing atmospheric water content that drives precipitation changes. Within the literature, general agreement is that extreme precipitation is changing, yet different assessment methods, data sets, and study periods may result in different patterns and rates of change. Here we perform a global analysis of 8,730 daily precipitation records focusing on the 1964–2013 period when the global warming accelerates. We introduce a novel analysis of the N largest extremes in records having N complete years within the study period. Based on these extremes, which represent more accurately heavy precipitation than annual maxima, we form time series of their annual frequency and mean annual magnitude. The analysis offers new insights and reveals (1) global and zonal increasing trends in the frequency of extremes that are highly unlikely under the assumption of stationarity and (2) magnitude changes that are not as evident. Frequency changes reveal a coherent spatial pattern with increasing trends being detected in large parts of Eurasia, North Australia, and the Midwestern United States. Globally, over the last decade of the studied period we find 7% more extreme events than the expected number. Finally, we report that changes in magnitude are not in general correlated with changes in frequency.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 it