  This paper introduces a novel visual analytics approach, DCPViz, to enable
climate scientists to explore massive climate data interactively without
requiring the upfront movement of massive data. Thus, climate scientists are
afforded more effective approaches to support the identification of potential
trends and patterns in climate projections and their subsequent impacts. We
designed the DCPViz pipeline to fetch and extract NEX-DCP30 data with minimal
data transfer from their public sources. We implemented DCPViz to demonstrate
its scalability and scientific value and to evaluate its utility under three
use cases based on different models and through domain expert feedback.
