---
license: apache-2.0
language:
- en
tags:
- OneScience
- Earth Science
- Climate Downscaling
- Diffusion
- Probabilistic Forecasting
- Coherence
frameworks: PyTorch
---
<p align="center"><strong><span style="font-size: 30px;">Climate2Weather</span></strong></p>
# Model Introduction
Climate2Weather uses conditional score diffusion to transform coarse climate simulations into probabilistic high-resolution weather trajectories.

Paper: A Generative Framework for Probabilistic, Spatiotemporally Coherent Downscaling of Climate Simulation  
https://doi.org/10.1038/s41612-025-01157-y
# Model Description
The method was proposed by the University of Tübingen and Tübingen AI Center. It was trained with 2006–2013 COSMO-REA6 reanalysis and conditions on climate-model fields only during inference. Score-based data assimilation jointly downscales four variables from `8x8` to `128x128` and from six-hourly to hourly resolution.
# Use Cases
| Use Case | Description |
|---|---|
| Probabilistic downscaling | Generate high-resolution ensemble trajectories. |
| Coherent generation | Jointly model variables and time. |
| Multivariate generation | Downscale wind, temperature, and sea-level pressure jointly. |
| Climate impacts | Generate fine-scale drivers for regional impact studies. |
| ModelScope/OneCode execution | Validate data, training, inference, metrics, and visualization. |
| Multi-GPU training | Start multi-process training through `torchrun`. |
# Usage Instructions
Use a GPU or DCU when available; CPU supports the default smoke configuration.
```bash
hf download OneScience-Group/Climate2Weather --local-dir ./Climate2Weather
cd Climate2Weather
python scripts/fake_data.py
```
For single-process training, use:
```bash
python scripts/train.py
```
For multi-process training, use:
```bash
torchrun --standalone --nproc_per_node=2 scripts/train.py
```
Run inference and evaluation with:
```bash
python scripts/inference.py
python scripts/result.py
```
Training minimizes denoising score matching. Inference produces a finite `[8,3,4,128,128]` ensemble with positive spread; evaluation reports RMSE, spread, PIT, and temporal differences.
## Trained Weights
No weights are bundled under `weight/`. The authors provide trained diffusion-model weights and experiment code at https://github.com/schmidtjonathan/Climate2Weather; this compact implementation does not claim compatibility.
# Citation and License
This repository is an independent engineering reproduction of the public Climate2Weather specifications.

The original paper is licensed under CC BY 4.0; the original paper, official code, model weights, and related data remain subject to their respective licenses and terms.