{
  "model_name": "ScaleAdaptiveCM",
  "model_type": "scale_adaptive_cm",
  "architectures": ["ScaleAdaptiveCM"],
  "framework": "PyTorch",
  "domain": "climate",
  "task": "probabilistic-precipitation-downscaling",
  "implementation": {
    "entry_point": "model/scale_adaptive_cm.py",
    "scope": "core-method, full-spatial-dimension reduced-model 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": 1, "output_channels": 1, "engineering_channels": [4, 8, 16], "paper_channels": [128, 128, 256, 256], "scale_factor": 4},
  "data": {"dataset": "ERA5 and ESM precipitation", "format_version": "scale_adaptive_cm_v1", "low_shape": ["B", 1, 60, 96], "high_shape": ["B", 1, 240, 384], "unit": "mm day-1", "synthetic": true},
  "configuration_sources": ["conf/config.yaml", "model/scale_adaptive_cm.py", "scripts/fake_data.py", "scripts/train.py", "scripts/inference.py", "scripts/result.py"]
}