Less Intense Daily Precipitation Maxima in Regional Compared to Global Gridded Products
Bibliographic record
Abstract
Abstract A consistent approach to evaluate the annual wettest day (Rx1day) across global and regional gridded observational datasets is presented using Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6) to define climatological regions. Global daily 1° × 1° latitude/longitude gridded products available from the Frequent Rainfall Observations on Grids (FROGS) database are compared with regional high-resolution (∼1–25 km) daily gridded rainfall datasets using several interpolation methods and order of operation. Climatologies are calculated for each global product and region using the overlapping period 2001–16, with global datasets then organized into in situ, satellite, and reanalysis groupings and compared with each other and the regional reference. Our findings show that reanalyses (especially CFSR and MERRA-2) tend to be among the wetter products for precipitation extremes in most regions and that reanalysis groupings also have the largest spread. Perhaps surprisingly, regional datasets are often among the drier, if not the driest products in many regions (especially Southeast Asia, Eurasia, and the Middle East), and are almost always drier than reanalyses except in a few cases. Rx1day is significantly positively correlated in most regions and products except in a handful of cases where data issues are likely to affect correlations. Rx1day timing deviates substantially between products, but agreement is highest among in situ products (40%–70% of the time) especially in data-dense regions with least agreement among reanalyses (10%–40% of the time). Despite uncertainties, the mean relative long-term trend estimates in Rx1day averaged across global land areas, with respect to increases in global mean temperature, are close to 7% °C −1 . Significance Statement While there have been numerous global and regional assessments of trends and variability of historical rainfall extremes, there has been little coordination or limited ability to compare across studies or to use regional products consistently when evaluating global products. Our results show for the first time that despite large uncertainty in the wettest day of the year [annual wettest day (Rx1day)] estimates, regional (continental scale) gridded precipitation products are consistently drier than their global counterparts. However, the trend in Rx1day averaged across all products is broadly consistent with the global increase of ∼7% °C −1 found in other studies. Given the huge spread in observations of daily precipitation extremes, our findings have implications for their efficacy in informing global monitoring, event attribution, and model evaluation efforts.
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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 | 0.000 |
| 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".