Time-varying volume data is often generated from scientific simulations in a variety of application domains, such as computational fluid dynamics, combustion science, and computational cosmology. Data visualization plays an important role in analyzing the dynamics and evolution of phenomena hidden in the data. Over the last two decades, a substantial amount of visualization techniques have been proposed in this research area. In this paper, we systematically review the recent literature on data visualization and visual analytics for time-varying scalar volume data. We first collect a corpus of relevant technical and application papers in visualization journals and conferences from 2008 to 2019. Based on this corpus, we classify these techniques into three aspects, including feature tracking, evolution visualization, and rendering, and then detaily describe relevant techniques in these three aspects. Finally, we conclude this survey with emerging trends and future challenges in time-varying volume visualization.