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Understanding the evolution and physical drivers of drought is critical to informing forecasting efforts. One aspect that has seldom been explored is the joint evolution of droughts in space and time. Most studies fix the reference area and focus on their temporal variability or study their spatial heterogeneity over fixed durations. This work implements a Lagrangian approach by aggregating contiguous areas under drought into clusters. These clusters become the frame of reference and are tracked as they evolve through space and time. Clusters were identified from soil moisture data from the Climate Forecast System Reanalysis (1979-2009). Evapotranspiration, moisture fluxes, and precipitation were used to explore the relevance of possible mechanisms of drought propagation. While most droughts remain near their origin, the centroid of 10% of clusters traveled at least 1400-3100km, depending on the continent. This approach also revealed that large-scale droughts often lock into further growth and intensification.

Plain Language Summary Droughts are some of the most expensive natural disasters that society has to face, so understanding how they evolve and what are the physical mechanisms that control them is critical for improving our ability to predict them. Past research has focused mostly on how droughts evolve in time over a fixed region and how droughts of a given duration evolve in space. However, little work has been done to study how they evolve in time and space, simultaneously, leading to a significant gap in our understanding of these extreme events. In this work we identify individual drought events between 1979 and 2009 around the world and track them in space and time to analyze their characteristics and behaviors across different continents. We find that some droughts can travel hundreds of kilometers from where they originated (e.g., from the U.S. Southwest to the U.S. Midwest), and that after droughts have grown and become intense enough, they will tend to become even larger and more intense before conditions improve (i.e., conditions will tend to become worse before they become better).