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Use the main script for parametric models (EXP_BernoulliGamma.py).

For new configurations, modify the CFG and CFGD files

  • CFG_* for the model architecture and training options (example in CFG_BernoulliGammaUNET.py).
  • CFGD_* for the data transformations (example in CFGD_StandardTransforms4Prediction_incX1D.py).

The script writes some stats after training and plots some maps automatically from the validation period for a quickcheck.

It also hashes the folders so there is no overwrite.

All the data necessary to reproduce the results in Legasa et al. (2026) can be found on Zenodo: https://doi.org/10.5281/zenodo.20610814.

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Code for reproducibility of the findings in Legasa et al. (2026), entitled "Regional Climate Model Emulation with Diffusion Approaches: What is the Added Value of Generative Machine Learning?", and submitted to Journal of Advances in Modeling Earth Systems (JAMES).

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