Accurate detection of precipitation on a global scale is essential for advancing our understanding of the hydrological cycle and improving climate models. This study evaluates the performance of the Rain Masking Algorithm (RMA), developed for NASA’s Micropulse Lidar Network (MPLNET), in detecting rainfall events and distinguishing them from non-rain events over multiple years. The RMA’s effectiveness was validated against data from co-located disdrometers at two distinct MPLNET sites: the Goddard Space Flight Center (GSFC) in the United States and Universitat Politècnica de Catalunya (UPC) in Barcelona, Spain. Comparisons were also conducted with precipitation retrievals from the Integrated Multi-Satellite Retrievals for GPM (IMERG) project. Results indicate that the RMA is highly effective at detecting rain events, outperforming IMERG in sensitivity and accuracy at both sites, and demonstrating also unique capability in distinguishing virga, precipitation that evaporated before reaching the ground (not considered in the intercomparison). However, the algorithm shows limitations in identifying low-intensity precipitation and occasionally records false positives due to transient atmospheric artifacts. These results underscore the potential of the RMA in advancing the validation of satellite precipitation data from the ground, which is advantageous for the upcoming ESA-JAXA EarthCARE mission. Although the current analysis does not include EarthCARE data, we present the performance of RMA and a corresponding matchup strategy that are intended to facilitate next validation efforts for EarthCARE’s precipitation data. This work also highlights the RMA as a promising tool for refining global precipitation monitoring and advancing meteorological and climate forecasting accuracy.

https://doi.org/10.1016/j.atmosres.2025.108535