A New Approach to Identifying and Analyzing Precipitation Events and Their Typical Lifecycles Over Conterminous United States

Precipitation is a key driver of the global water cycle and energy circulation, yet its complex formation and dynamic lifecycle make both observation and simulation challenging. Tracking precipitation events and analyzing their lifecycle stages (development, maturity, and dissipation) are essential for understanding precipitation dynamics. This study proposes a morphological precipitation event extraction (MPEE) method and a lifecycle fitting model, applied to CONUS404, Integrated Multi-satellitE Retrievals for GPM, and ECMWF Reanalysis 5 from 2001 to 2021. Intercomparison results confirm the method's robustness, with most correlation coefficients exceeding 0.5 and most root mean squared errors less than 0.7 mm/hr, between extracted events and simulated life cycles. K-means clustering reveals four precipitation types: common, high-peak, long-duration, and slow-developing events with delayed peaks. The method effectively captures precipitation variability across data sets and provides a scalable approach for studying long-term precipitation trends. This work lays a foundation for analyzing climate-scale precipitation lifecycle changes, improving our understanding of precipitation dynamics and their implications for climate variability.