<p>In oceanic and atmospheric science, finer resolutions have become a prevailing trend in all aspects of development. For high-resolution fluid flow simulations, the computational costs of widely used numerical models increase significantly with the resolution. Artificial intelligence methods have attracted increasing attention because of their high precision and fast computing speeds compared with traditional numerical model methods. The resolution-independent Fourier neural operator (FNO) presents a promising solution to the still challenging problem of high-resolution fluid flow simulations based on low-resolution data. Accordingly, we assess the potential of FNO for high-resolution fluid flow simulations using the vorticity equation as an example. We assess and compare the performance of FNO in multiple high-resolution tests varying the amounts of data and the evolution durations. When assessed with finer resolution data (even up to number of grid points with 1 280 × 1 280), the FNO model, trained at low resolution (number of grid points with 64 × 64) and with limited data, exhibits a stable overall error and good accuracy. Additionally, our work demonstrates that the FNO model takes less time than the traditional numerical method for high-resolution simulations. This suggests that FNO has the prospect of becoming a cost-effective and highly precise model for high-resolution simulations in the future. Moreover, FNO can make longer high-resolution predictions while training with less data by superimposing vorticity fields from previous time steps as input. A suitable initial learning rate can be set according to the frequency principle, and the time intervals of the dataset need to be adjusted according to the spatial resolution of the input when training the FNO model. Our findings can help optimize FNO for future fluid flow simulations.</p>