---
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.