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Co-authored-by: Cursor <cursoragent@cursor.com>

README.md

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+---
+license: mit
+task_categories:
+  - image-to-image
+tags:
+  - climate
+  - downscaling
+  - super-resolution
+  - weather
+  - meteorology
+  - deep-learning
+language:
+  - en
+size_categories:
+  - 10K<n<100K
+---
+
+# Climate Downscaling Dataset (S2S)
+
+A dataset for climate data spatial downscaling from low resolution (16×16) to high resolution (64×64).
+
+## Dataset Description
+
+This dataset contains climate variables for training deep learning models to perform statistical downscaling. The data is preprocessed and ready for use with PyTorch-based frameworks.
+
+### Data Format
+
+The dataset consists of PyTorch tensor files (`.pt`) with the following structure:
+
+| Key | Shape | Description |
+|-----|-------|-------------|
+| `LR_input` | `[C, T, 16, 16]` | Low-resolution input (C=7 channels, T=time steps) |
+| `HR_target` | `[C, T, 64, 64]` | High-resolution target |
+| `HR_topo` | `[2, 64, 64]` | Topographic data (elevation and slope) |
+
+### Files
+
+| File | Size | Description |
+|------|------|-------------|
+| `dict_s2s_train.pt` | 8.6 GB | Training set |
+| `dict_s2s_test.pt` | 1.8 GB | Test set |
+| `dict_s2s_val.pt` | 873 MB | Validation set |
+| `HR_topo.nc` | 58 KB | Topographic data (NetCDF format) |
+
+### Input Channels
+
+The 7 input channels in `LR_input` typically include:
+- Temperature (t2m)
+- Geopotential height
+- U-wind component
+- V-wind component
+- Relative humidity
+- Surface pressure
+- Other meteorological variables
+
+### Scale Factor
+
+- **Input resolution**: 16×16
+- **Output resolution**: 64×64
+- **Scale factor**: 4x
+
+## Usage
+
+### Loading the Data
+
+```python
+import torch
+
+# Load training data
+train_data = torch.load('dict_s2s_train.pt')
+
+lr_input = train_data['LR_input']    # [C, T, 16, 16]
+hr_target = train_data['HR_target']  # [C, T, 64, 64]
+hr_topo = train_data['HR_topo']      # [2, 64, 64]
+
+# Transpose to [T, C, H, W] for batch processing
+lr_input = lr_input.permute(1, 0, 2, 3)
+hr_target = hr_target.permute(1, 0, 2, 3)
+
+print(f"Number of samples: {lr_input.shape[0]}")
+print(f"Input shape: {lr_input.shape}")
+print(f"Target shape: {hr_target.shape}")
+```
+
+### With Hugging Face Datasets
+
+```python
+from huggingface_hub import hf_hub_download
+
+# Download specific file
+train_path = hf_hub_download(
+    repo_id="YOUR_USERNAME/climate-downscaling-s2s",
+    filename="dict_s2s_train.pt",
+    repo_type="dataset"
+)
+
+train_data = torch.load(train_path)
+```
+
+## Citation
+
+If you use this dataset, please cite:
+
+```bibtex
+@dataset{climate_downscaling_s2s,
+  title={Climate Downscaling Dataset for Deep Learning},
+  year={2025},
+  url={https://huggingface.co/datasets/YOUR_USERNAME/climate-downscaling-s2s}
+}
+```
+
+## License
+
+This dataset is released under the MIT License.