  Efficiently modeling spatio-temporal (ST) physical processes and observations
presents a challenging problem for the deep learning community. Many recent
studies have concentrated on meticulously reconciling various advantages,
leading to designed models that are neither simple nor practical. To address
this issue, this paper presents a systematic study on existing shortcomings
faced by off-the-shelf models, including lack of local fidelity, poor
prediction performance over long time-steps,low scalability, and inefficiency.
To systematically address the aforementioned problems, we propose an
EarthFarseer, a concise framework that combines parallel local convolutions and
global Fourier-based transformer architectures, enabling dynamically capture
the local-global spatial interactions and dependencies. EarthFarseer also
incorporates a multi-scale fully convolutional and Fourier architectures to
efficiently and effectively capture the temporal evolution. Our proposal
demonstrates strong adaptability across various tasks and datasets, with fast
convergence and better local fidelity in long time-steps predictions. Extensive
experiments and visualizations over eight human society physical and natural
physical datasets demonstrates the state-of-the-art performance of
EarthFarseer. We release our code at
https://github.com/easylearningscores/EarthFarseer.
