NeuralGCM is a hybrid ML + physics global circulation model: a differentiable spectral dynamical core coupled to learned physics, for medium-range weather forecasting and decade-long climate simulation originally developed in JAX. This repository hosts the published NeuralGCM v1 checkpoints converted to PyTorch, ready to load with neuralgcm-torch — real nn.Modules with registered parameters, no JAX, gin or haiku at runtime and no conversion step at load time.

The default Hub repo is it4lia/neuralgcm-torch — override per call with repo_id= or globally with NEURALGCM_TORCH_HF_REPO.

Rules of thumb: the 1.4° stochastic model is the best general-purpose weather/ensemble model, the 2.8° models are stable for multi-decade climate runs, and the 0.7° model is the most accurate at short lead times. Each was converted once, offline from the original JAX pickle (gin config + haiku parameter tree) into a plain torch.save dictionary — structured config, auxiliary arrays, and parameter tensors.

Runnable example notebooks — forecasting at every resolution, batched ensembles, precipitation/evaporation, multi-decade climate stability, and the model internals — are rendered as a Jupyter Book at dsip-fbk.github.io/neuralgcm-torch. Source code, the full API and design notes live in the GitHub repository.

These weights are derivative works of the NeuralGCM v1 checkpoints published by Google at gs://neuralgcm/models/ under CC BY-SA 4.0. As required by ShareAlike, they are redistributed under the same license (CC BY-SA 4.0).

This is an independent reimplementation, not affiliated with the original NeuralGCM authors or Google. The neuralgcm-torch model code is separately licensed under Apache 2.0.

The PyTorch port and checkpoint conversion were developed at Fondazione Bruno Kessler (FBK) and are hosted on the IT4LIA AI Factory.