Enormous efforts have been recently made to super-resolve hyperspectral (HS) images with the aid of high spatial resolution multispectral (MS) images. Most prior works usually perform the fusion task by means of multifarious pixel-level priors. Yet, the intrinsic effects of a large distribution gap between HS–MS data due to differences in the spatial and spectral resolution are less investigated. The gap might be caused by unknown sensor-specific properties or highly mixed spectral information within one pixel (due to low spatial resolution). To this end, we propose a subpixel-level HS super-resolution (HS-SR) framework by devising a novel decoupled-and-coupled network (DC-Net), to progressively fuse HS–MS information from the pixel level to subpixel level and from the image level to feature level. As the name suggests, DC-Net first decouples the input into common (or cross-sensor) and sensor-specific components to eliminate the gap between HS–MS images before further fusion and then thoroughly blends them by a model-guided coupled spectral unmixing (CSU) net. More significantly, we append a self-supervised learning module behind the CSU net by guaranteeing material consistency to enhance the detailed appearance of the restored HS product. Extensive experimental results show the superiority of our method both visually and quantitatively and achieve a significant improvement in comparison with the state of the art (SOTA).