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This paper introduces a novel visual analytics approach, DCPViz, to enableclimate scientists to explore massive climate data interactively withoutrequiring the upfront movement of massive data. Thus, climate scientists areafforded more effective approaches to support the identification of potentialtrends and patterns in climate projections and their subsequent impacts. Wedesigned the DCPViz pipeline to fetch and extract NEX-DCP30 data with minimaldata transfer from their public sources. We implemented DCPViz to demonstrateits scalability and scientific value and to evaluate its utility under threeuse cases based on different models and through domain expert feedback.

DCPViz: A Visual Analytics Approach for Downscaled Climate Projections