Globally, extreme precipitation events cause enormous impacts. Climate change increases the frequency and intensity of extreme precipitation, which in combination with rising population enhances exposure to major floods. An improved understanding of the atmospheric processes that cause extreme precipitation events would help to advance predictions and projections of such events. To date, such analyses have typically been performed rather unsystematically and over limited areas (e.g., the U.S.) which has resulted in contradictory findings. Here we present the Multi Object Analysis of Atmospheric Phenomenon (MOAAP) algorithm that uses a set of nine common atmospheric variables to identify and track tropical and extra-tropical cyclones, anticyclones, atmospheric rivers (ARs), mesoscale convective systems (MCSs), and frontal zones. We apply the algorithm to global historical data between 2000 to 2020. We find that MCSs produce the vast majority of extreme precipitation in the tropics and some mid-latitude land regions, while extreme precipitation in mid- and high-latitude ocean and coastal regions are dominated by cyclones and ARs. Importantly, most extreme precipitation events are associated with interacting features across scales that intensify precipitation. These interactions, however, can be a function of the rarity (e.g., return period) of extreme events. The presented methodology and results could have wide-ranging applications including training of machine learning methods, lagrangian-based evaluation of climate models, and process-based understanding of extreme precipitation in a changing climate.