  Numerical models have long been used to understand geoscientific phenomena,
including tidal currents, crucial for renewable energy production and coastal
engineering. However, their computational cost hinders generating data of
varying resolutions. As an alternative, deep learning-based downscaling methods
have gained traction due to their faster inference speeds. But most of them are
limited to only inference fixed scale and overlook important characteristics of
target geoscientific data. In this paper, we propose a novel downscaling
framework for tidal current data, addressing its unique characteristics, which
are dissimilar to images: heterogeneity and local dependency. Moreover, our
framework can generate any arbitrary-scale output utilizing a continuous
representation model. Our proposed framework demonstrates significantly
improved flow velocity predictions by 93.21% (MSE) and 63.85% (MAE) compared to
the Baseline model while achieving a remarkable 33.2% reduction in FLOPs.
