  The identification of regions of similar climatological behavior can be
utilized for the discovery of spatial relationships over long-range scales,
including teleconnections. In this regard, the global picture of the
interdependence patterns of extreme rainfall events (EREs) still needs to be
further explored. To this end, we propose a top-down complex-network-based
clustering workflow, with the combination of consensus clustering and mutual
correspondences. Consensus clustering provides a reliable community structure
under each dataset, while mutual correspondences build a matching relationship
between different community structures obtained from different datasets. This
approach ensures the robustness of the identified structures when multiple
datasets are available. By applying it simultaneously to two satellite-derived
precipitation datasets, we identify consistent synchronized structures of EREs
around the globe, during boreal summer. Two of them show independent
spatiotemporal characteristics, uncovering the primary compositions of
different monsoon systems. They explicitly manifest the primary intraseasonal
variability in the context of the global monsoon, in particular the `monsoon
jump' over both East Asia and West Africa and the mid-summer drought over
Central America and southern Mexico. Through a case study related to the Asian
summer monsoon (ASM), we verify that the intraseasonal changes of upper-level
atmospheric conditions are preserved by significant connections within the
global synchronization structure. Our work advances network-based clustering
methodology for (i) decoding the spatiotemporal configuration of
interdependence patterns of natural variability and for (ii) the
intercomparison of these patterns, especially regarding their spatial
distributions over different datasets.
