Global Location Transferability of Generative Deep Learning Models for Precipitation Downscaling
Bibliographic record
Abstract
Generative deep learning models have shown remarkable skill in the probabilistic downscaling of climate and weather forecasts, with generative adversarial networks (GANs) as a particularly effective approach for precipitation downscaling. However, most existing methods are trained for specific regions, and their performance on unseen geographic areas remains largely unexplored. In our work, we evaluate the transferability of generative models to new locations outside their training domain. Using a global experimental setup, we employ ERA5 as the predictor dataset and IMERG as the high-resolution target dataset at 0.1° resolution. To systematically assess the performance across diverse regions, we design a hierarchical location split with 16 regions. We then train networks independently on the 16 regions and evaluate each of them on all others. Our findings provide insights on the robustness and limitations of generative models for global-scale precipitation downscaling, revealing challenges such as poor generalization to unseen orography and decreased performance in tropical regions, both for models applied in these areas and for those trained in the tropics and transferred elsewhere.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".