Flash droughts have recently gained significant attention due to their severe economic and ecological impacts. Despite extensive and growing research on flash drought processes, predictability, and trends, there is still no standard quantitative definition that encompasses all flash drought characteristics and pathways. This has motivated efforts to define, inventory, monitor, and forecast flash drought events. In our recent studies of flash droughts over the United States, we have introduced the Soil Moisture Volatility Index definition (SMVI) to inventory the onset dates and severity of flash across the Contiguous United States (CONUS) for the period 1979-2018. Post to an extended evaluation and comparison to other flash drought definitions and independent vegetation and crop datasets for seminal flash drought events, the SMVI has proved effectiveness in capturing flash drought onset in both humid and semi-arid regions. Using our SMVI inventory of flash droughts, we examine and classify flash droughts events based on multiple land surface and atmospheric conditions that may represent predictable drivers using a K-means-based clustering methodology. We found that there are three distinct classes of flash drought that can be diagnosed in our inventory. The first defined class of events are the “dry and demanding” droughts, showing high anomalies of evaporative demand and low soil moisture levels; The second are “evaporative” events, which develop under conditions of high demand and when elevated evapotranspiration accelerates soil drying, and a third class that we refer to as “stealth” events, which may be challenging to predict based on precursor atmospheric conditions due to the lack of a clear atmospheric signal with the observed modest anomalies. The contrasting meteorological and surface process signatures of the three classes do, however, indicate that events identified as “flash drought” using a reasonable definition, including events that have been widely reported as seminal flash droughts, represent a diversity of onset and intensification processes. Our results suggest that recognizing this diversity is critical to advance our understanding and ability to predict these events.