Catalogs of atmospheric rivers (ARs) are vital resources to advance AR science. However, identification of ARs at a global scale comes along with substantial challenges caused by regionally and temporally varying weather systems. Most available AR catalogs have regional extent, although only global AR catalogs can record large-scale heterogeneities in AR transport. Here, we introduce the PIK Atmospheric River Trajectories (PIKART) catalog, a global and comprehensive compilation of AR activity covering 84 years (1940 to 2023) with a high spatiotemporal resolution of 0.5° and 6 hours. PIKART identifies ARs by exploiting their anomalous moisture transport characteristics and is, therefore, threshold-free. Moreover, it tracks ARs prioritizing large and strong features, and allowing for physically-sound temporal gaps, ultimately improving the representation of long-lived ARs. PIKART substantially extends the scope of previous catalogs by providing secondary AR properties such as a novel index of inland penetration, land-intersecting locations, and AR levels. Available as a compilation of AR conditions and AR trajectories, PIKART facilitates the study of ARs from both the Eulerian and Lagrangian perspective. As a first overview of the catalog’s scope, we use PIKART to reveal i) additional hotspots of AR activity, particularly in the tropics, ii) exposure to considerable AR impacts in less-studied continents (e.g., South/East Asia and Oceania), iii) inland penetration of ARs into less-studied regions (e.g., north-western Africa), and iv) a poleward shift of southern hemispheric ARs and a global intensification of AR moisture transport. The PIKART catalog constitutes a valuable resource for future studies in AR science.

The spatiotemporal patterns of precipitation are critical for understanding the underlying mechanism of many hydrological and climate phenomena. Over the last decade, applications of the complex network theory as a data-driven technique has contributed significantly to study the intricate relationship between many variable in a compact way. In our work, we conduct a study to compare an extreme precipitation pattern in Ganga River Basin, by constructing the networks using two nonlinear methods - event synchronization (ES) and edit distance (ED). Event synchronization has been frequently used to measure the synchronicity between the climate extremes like extreme precipitation by calculating the number of synchronized events between two events like time series. Edit distance measures the similarity/dissimilarity between the events by reducing the number of operations required to convert one segment to another, that consider the events’ occurrence and amplitude. Here, we compare the extreme precipitation patterns obtained from both network construction methods based on different network’s characteristics. We used degree to understand network topology and identify important nodes in the networks. We also attempted to quantify the impact of precipitation seasonality and topography on extreme events. The study outcomes suggested that the degree is decreased in the southwest to the northwest direction and the timing of peak precipitation influences it. We also found an inverse relationship between elevation and timing of peak precipitation exists and the lower elevation greatly influences the connectivity of the stations. The study highlights that Edit distance better captures the network’s topology without getting affected by artificial boundaries.