  FourCastNeXt is an optimization of FourCastNet - a global machine learning
weather forecasting model - that performs with a comparable level of accuracy
and can be trained using around 5% of the original FourCastNet computational
requirements. This technical report presents strategies for model optimization
that maintain similar performance as measured by the root-mean-square error
(RMSE) of the modelled variables. By providing a model with very low
comparative training costs, FourCastNeXt makes Neural Earth System Modelling
much more accessible to researchers looking to conduct training experiments and
ablation studies. FourCastNeXt training and inference code are available at
https://github.com/nci/FourCastNeXt
