The potential for increasing frequency and intensity of extreme phenomena including downpours, heat waves, and tropical cyclones constitutes one of the primary risks of climate change for society and the environment. The challenge of characterizing these risks is that extremes represent the "tails" of distributions of atmospheric phenomena and are, by definition, highly localized and typically relatively transient. Therefore very large volumes of observational data and projections of future climate are required to quantify their properties in a robust manner. Massive data analytics are required in order to detect individual extremes, accumulate statistics on their properties, quantify how these statistics are changing with time, and attribute the effects of anthropogenic global warming on these statistics.

We describe examples of the suite of techniques the climate community is developing to address these analytical challenges. The techniques include massively parallel methods for detecting and tracking atmospheric rivers and cyclones; data-intensive extensions to generalized extreme value theory to summarize the properties of extremes; and multi-model ensembles of hindcasts to quantify the attributable risk of anthropogenic influence on individual extremes. We conclude by highlighting examples of these methods developed by our CASCADE (Calibrated and Systematic Characterization, Attribution, and Detection of Extremes) project.