Cloud cover affects many important ecological processes, including reproduction, growth, survival, and behavior. When quantified globally at high spatial resolution, cloud cover dynamics can provide key information for delineating a variety of habitat types and predicting species distributions. In this study, we develop a new near-global, fine-grain dataset of monthly cloud frequencies from 15y of twice-daily satellite images. The new data reveal cloud cover dynamics at unprecedented spatial resolution. We show that the direct, observation-based nature of cloud-derived metrics can improve predictions of habitats, ecosystem, and species distributions with reduced spatial autocorrelation compared to commonly used interpolated climate data.