Generative adversarial networks for downscaling hourly precipitation in the canadian prairies
Bibliographic record
Abstract
Developing robust downscaling methods is essential for maximizing the applicability of climate model outputs in engineering design and climate mitigation, particularly in a changing climate. This study evaluates four deep learning model configurations for downscaling, focusing on their structure, functionality, and ability to capture localized convective events in the Canadian prairies. These model configurations aim to downscale coarse‐resolution climate model outputs (∼200 km) to the finer spatial resolution of regional climate models (∼50 km) for hourly precipitation. We introduce advanced metrics to assess the fidelity of precipitation downscaling, examining both marginal statistics and spatiotemporal dependencies. A U‐Network (UNET) captures spatial and temporal dependencies efficiently while three generative adversarial networks (GANs) configurations incorporate a critic network to enhance the realism of generated fields. The study also evaluates the effects of a thresholding layer to constrain precipitation values and a convolution long short‐term memory layer in the GAN critic to better capture temporal dependencies. Results show that all four model configurations effectively capture spatial dependencies, with the simplest GAN architecture outperforming others in preserving temporal dependencies. Latitudinal correlations are better preserved than longitudinal across all models. While UNET produces overly smoothed fields, GANs generate more detailed outputs when downscaling Coupled Model Intercomparison Project phase 6 projections. By optimizing deep learning models for this region, the study provides key insights into future precipitation trends, enabling the identification of localized storms. These findings are critical for improving infrastructure resilience across catchments in the prairies.
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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 | 0.005 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".