config.json

{
  "model_name": "PrecipExtremes-GAN",
  "model_type": "precip_extremes_gan",
  "architectures": ["PrecipExtremesGAN"],
  "framework": "PyTorch",
  "domain": "climate",
  "task": "extreme-precipitation-downscaling",
  "implementation": {"entry_point": "model/precip_extremes_gan.py", "scope": "core-method reduced-sample engineering reproduction", "train_script": "scripts/train.py", "inference_script": "scripts/inference.py", "evaluation_script": "scripts/result.py", "synthetic_data_script": "scripts/fake_data.py"},
  "architecture": {"input_channels": 8, "output_channels": 1, "baseline": "residual U-Net", "generator": "stochastic residual U-Net", "critic": "conditional patch critic", "paper_filters": [32,64,128]},
  "data": {"dataset": "CCAM New Zealand perfect-framework simulations", "format_version": "precip_extremes_gan_v1", "input_variables": ["U500","U850","V500","V850","T500","T850","Q500","Q850"], "target": "daily precipitation", "target_unit": "mm day-1", "engineering_low_grid": [24,24], "engineering_high_grid": [96,96], "grid_status": "engineering assumption because final tensor dimensions are not reported", "synthetic": true},
  "configuration_sources": ["conf/config.yaml", "model/precip_extremes_gan.py", "scripts/fake_data.py", "scripts/train.py", "scripts/inference.py", "scripts/result.py"]
}