OneScience-Group/FuXi-S2S

FuXi-S2S

Model Introduction

FuXi-S2S is a global subseasonal forecasting model proposed by researchers from Fudan University and collaborating institutions.

Paper: A machine learning model that outperforms conventional global subseasonal forecast models

https://doi.org/10.1038/s41467-024-50714-1

Model Description

FuXi-S2S takes two consecutive daily mean atmospheric states as input and targets the two-week to two-month forecast range, where conventional numerical models remain challenging to use effectively. This model package exposes the official ONNX inference graph through a small ONNX Runtime adapter.

Use Cases

Scenario	Description
Global subseasonal forecasting	Run the official FuXi-S2S ONNX weights with ERA5 inputs following the fixed 76-channel order.
Local quick validation	Use synthetic HDF5 data to check data loading, ONNX Runtime execution, and visualization.
ModelScope / OneCode execution	Download the standalone model package, configure an ONNX Runtime provider, and run the scripts directly.

Usage Guide

1. OneCode Usage

Experience intelligent one-click AI4S programming through the OneCode online environment:

Click to Experience Intelligent One-Click AI4S Programming

2. Manual Installation and Usage

Hardware Requirements

• A GPU or DCU is recommended for practical inference. CPU can be used for import and small-scale connectivity checks, but full-resolution inference will be slow.
• Install the ONNX Runtime build that provides the execution provider required by your hardware.
• DCU users must install DTK in advance. DTK 25.04.2 or above, or the OneScience recommended version matching your cluster, is recommended.

Download the Model Package

hf download OneScience-Group/FuXi-S2S --local-dir ./FuXi-S2S
cd FuXi-S2S

Install the Runtime Environment

DCU Environment

# Please activate DTK and CONDA first
conda create -n onescience311 python=3.11 -y
conda activate onescience311
# uv installation is supported
pip install onescience[earth-dcu] -i http://mirrors.onescience.ai:3141/pypi/simple/  --trusted-host mirrors.onescience.ai

GPU Environment

# Please activate CONDA first
conda create -n onescience311 python=3.11 -y libstdcxx-ng=12 libgcc-ng=12 gcc_linux-64=12 gxx_linux-64=12
conda activate onescience311
# uv installation is supported
pip install onescience[earth-gpu] -i http://mirrors.onescience.ai:3141/pypi/simple/  --trusted-host mirrors.onescience.ai

Install or select an ONNX Runtime provider matching the target hardware, then update model.providers in conf/config.yaml if necessary. The default configuration targets a DCU-compatible provider list.

Training Data Introduction

The official model uses daily mean ERA5 states with a fixed 76-channel order. The OneScience community provides an ERA5 data slice:

hf download --repo-type dataset OneScience-Group/ERA5 --local-dir ./data

The adapter expects yearly files under data/data/ and normalization arrays under data/stats/. Confirm the variable order in conf/config.yaml before inference.

Generate Synthetic Data

When real ERA5 data is unavailable, generate native-grid HDF5 files for interface checks:

python scripts/fake_data.py

For a smaller smoke fixture, pass --height 32 --width 64; synthetic data does not reproduce the official forecast quality.

Pre-trained Weights

The official ONNX graph requires both files below:

weight/fuxi_s2s.onnx
weight/fuxi_s2s

The large weight files are not bundled in this working copy and must be supplied from the authorized release. The weight/ directory is reserved for these files.

Inference

Inference reads weight/fuxi_s2s.onnx and its external data file by default. It converts ERA5 fields to the model's 121x240 grid and writes ONNX outputs to result/output/:

python scripts/inference.py

Use --device cpu, --device cuda, or --device dcu and configure model.providers for the selected runtime.

Evaluation and Visualization

python scripts/result.py

The result script reads the newest NPY output and writes multi-variable forecast figures to result/visualization/.

Official OneScience Resources

Platform	OneScience Main Repository	Skills Repository
Gitee	https://gitee.com/onescience-ai/onescience	https://gitee.com/onescience-ai/oneskills
GitHub	https://github.com/onescience-ai/OneScience	https://github.com/onescience-ai/oneskills

Citation and License

• This model package contains an adapter for the official FuXi-S2S ONNX release.
• The official ONNX graph, external data file, and related data are subject to the CC BY-NC-ND 4.0 terms stated by the authorized release.