Neural Radiance Fields (NeRF) have achieved remarkable progress on dynamic scenes with deformable objects. Nonetheless, most of the previous works required multi-view inputs or long training time (several hours), making it hard to apply them for real-world scenarios. Recently, a series of works have been dedicated to addressing the blurry artifact present in synthesized novel views given a monocular input of dynamic scenes. However, they may fail to predict stable and accurate deformation while keeping high-frequency details when rendering at various resolutions. To this end, we introduce a novel framework DMiT (Deformable Mipmapped Tri-Plane) that adopts the mipmaps to render dynamic scenes with various resolutions from novel views. With the help of hierarchical mipmapped triplanes, we incorporate an MLP to effectively predict a mapping between the observation space and the canonical space, enabling not only high-fidelity dynamic scene rendering but also high-performance training and inference. Moreover, a training scheme for joint geometry and deformation refinement is designed for canonical regularization to reconstruct high-quality geometries. Extensive experiments on both synthetic and real dynamic scenes demonstrate the efficacy and efficiency of our method.