**English** · [简体中文](https://github.com/GISWLH/WeatherAI/blob/main/README_zh.md) · [日本語](https://github.com/GISWLH/WeatherAI/blob/main/README_ja.md)
[](https://github.com/GISWLH/WeatherAI/blob/main/LICENSE)
[](https://github.com/GISWLH/WeatherAI/blob/main/pyproject.toml)
[](https://pytorch.org)
[](models/neuralgcm.md)
[](#model-zoo)
[](https://huggingface.co/spaces/LonghaoWang/weatherai-graphcast-smoke)
[](https://huggingface.co/LonghaoWang/WeatherAI)
[](https://github.com/GISWLH/WeatherAI/stargazers)
**WeatherAI** re-implements the AI weather and climate models published in *Nature*, *Science* and their sister
journals as plain, readable PyTorch `nn.Module`s, and checks every port against the authors' own code.
Load the official weights, compare with the original, fine-tune, or start from a Lite config that runs on a laptop.
## ✨ Highlights
**19 Oct 2026 research update:** [PBC probabilistic S2S calibration, GenFocal official pretrained
downscaling, and gray-swan translocation diagnostics](https://github.com/GISWLH/WeatherAI/blob/main/docs/research_20261019.md)
use pinned official code and published weights by default, with explicit separate lite variants.
[Actual execution reports](https://github.com/GISWLH/WeatherAI/blob/main/docs/research_20261019.md#actual-validation) distinguish pretrained
GPU inference from the earlier lite-only synthetic smoke. WeatherPre defaults to the official entries.
PBC and the diagnostic wrapper extend the existing 20-model zoo; they are not
two additional pretrained weather forecasters.
```python
from weatherai import PBC, PBC_lite, GenFocal, GenFocal_lite, Translocation, Translocation_lite
```
- 🧩 **20 models, one library** — Pangu, GraphCast, Aurora, GenCast, NeuralGCM, FuXi-S2S, CAS-Canglong and more, all
with the same `import` style and plain tensors in / tensors out. No per-model environments to juggle.
- 🔀 **Multi-framework** — native PyTorch throughout; official **JAX** code is kept where it matters (NeuralGCM training and
precipitation) and as the reference for GraphCast, GenCast and WeatherNext; **ONNX**-only releases (FuXi-ENS, FuXi-S2S)
are rebuilt as PyTorch modules. Runs on CPU and **CUDA** GPUs.
- ✅ **Verified, not just rewritten** — official checkpoints strict-load for 14 models, and outputs match the official
implementation (often bit-for-bit). Every claim has a script and a JSON result; GPU runs are tested on an NVIDIA
Blackwell GPU through a Hugging Face ZeroGPU Space.
- 🪶 **Full size and Lite** — the paper-size network for real experiments, plus Lite configs (same code, small grid) for 15
models that train on a laptop CPU or a single small GPU.
## 🚀 Quick start
```bash
pip install git+https://github.com/GISWLH/WeatherAI.git # or: git clone ... && pip install -e ".[dev]"
```
```python
import torch
from weatherai.models import Canglong_lite, GraphCast_lite, Aurora_small
sst = Canglong_lite()(torch.randn(1, 8, 8, 32, 64)) # 3-month SST outlook -> (1, 1, 3, 32, 64)
step = GraphCast_lite()(torch.randn(1, 4, 32, 64)) # one 6-hour step on a tiny icosahedral mesh
aurora = Aurora_small(pretrained=True) # official weights (~450 MB), strict-loaded into native PyTorch
```