Calibrated coefficients for high-resolution downscaling: A 1-km gridded daily dataset of temperature and precipitation across the Contiguous United States from NMME Seasonal forecasts
Bibliographic record
Abstract
The coarse resolution of the North American Multi-Model Ensemble (NMME) often introduces biases and uncertainties when applied to regional and local scales, limiting its applications in crop modeling and irrigation management. To address these limitations, we employed a statistical downscaling method with bias correction for both mean and variability. This approach was applied to the Canadian Coupled Climate Model version 4 (CanCM4), a representative model within the NMME, to generate 1-km gridded daily weather projections for maximum and minimum air temperatures and precipitation across the contiguous United States (CONUS). The downscaled hindcast projections were calibrated using the Daily Surface Weather and Climatological Summaries (DAYMET) dataset. This dataset provides the calibrated coefficients necessary to produce 1-km gridded daily weather projections, offering a valuable resource for applications such as regional crop modeling and irrigation management. Details can be found in our paper: Su, Q., Ale, S., Himanshu, S., Singh, J., and Singh, V.P. (2025). Calibration and bias correction of seasonal weather forecasts from the North American Multi-Model Ensemble: Potential applications for regional crop modeling and irrigation management. Journal of Agricultural Science 1-14. https://doi.org/10.1017/S0021859625000139
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".