Increased computational power has facilitated a transition toward atmospheric models with ever finer grid spacing. These high-resolution models reproduce the observed structure of clouds and precipitation much better than coarse-resolution global climate models, but this improved output accuracy comes with a much higher volume of output data. Advanced visualization techniques would provide a means of more thoroughly exploring these output data, and we outline three themes for which visualization has the potential to yield new insights in clouds and climate research: spatiotemporal renders to understand cloud evolution, spatial renders to build intuition about cloud structures, and topological data analysis to more rigorously evaluate simulations. We demonstrate four use cases with storm-resolving model output and discuss the benefits and challenges associated with these visualizations. These approaches avoid limitations of more standard visualizations, such as loss of information upon averaging, biases from regridding, and handling of spatial offset in model evaluation. Such methods could also inform the observational sampling necessary to represent certain phenomena. They do pose challenges, however, including the incorporation of uncertainty, management of computational costs, and workflow standardization. In summary, we advocate for strategic use of volume rendering and topological analyses, alongside traditional methods, to extract additional insight from high-resolution model output.