My research uses the combined tools of climate dynamics, statistics, and machine learning to describe, understand, and predict climate variability and change. The overarching motivation of my research program is to reduce uncertainty about the future climate through developing interpretable, predictive models that can be validated using available data. This information is essential for policymakers and planners, particularly in climate-vulnerable regions. The three major scientific goals of my work are to quantify the complex natural variability in the climate system, to develop a process-level understanding of regional climate change, and to improve climate predictions from subseasonal to centennial timescales. My work bridges the weather-climate gap by moving from a large-scale, global-average view of climate to one that sheds insight on local changes, including in extreme events, which determine how individuals and societies experience climate change.