Precipitation processes are among the most complex manifestations of Earth’s hydrometeorological system. Understanding of rain and snow variability across the scales are crucial challenges of hydrological science and applications. As our ability to accurately monitor and model precipitation processes is improving, it has become clear in recent years that advanced approaches including AI/ML have the potential to meet increasing requirements for wide range of hydrometeorological studies. This session welcomes contributions on precipitation-related analyses, including AI/ML, seeking physical implications, model interpretations, remote sensing applications and uncertainty analyses that can identify novel elements in understanding and applications of precipitation processes and their variability. We call for studies that can link the information content of observational/modeled datasets to the physical causes, materializations and variability of precipitation processes across various scales. These include, but are not limited to, studies on radiative-, hydrometeor-, cloud-, and hydrological-responses to precipitation processes.