The mesoscale organisation of marine stratocumulus clouds into closed and open cells strongly affects cloud albedo and thus their cooling effect on climate, yet the processes governing transitions between these regimes remain incompletely understood. The EarthCARE satellite provides collocated observations of cloud mesoscale structure from the Multi-Spectral Imager (MSI) together with vertically resolved cloud and precipitation measurements from the Atmospheric Lidar (ATLID) and Cloud Profiling Radar (CPR), enabling detailed characterization of stratocumulus cloud microphysics. We apply a convolutional neural network to MSI scenes to identify closed and open cells and relate these classifications to EarthCARE microphysical retrievals from the synergy of ATLID, CPR, and MSI. Open cells exhibit substantially lower droplet number concentrations (<em>N_d</em>), greater variability in liquid water path (LWP) and droplet sizes (<em>r_e</em>), and more frequent and heavier precipitation, although light drizzle is also common in closed cells. To investigate closed-to-open cell transitions, we combine EarthCARE overpasses with GOES/ABI geostationary imagery and ERA5-driven trajectories to track cloud scenes and determine transition timing. This combined approach allows us to reconstruct the temporal evolution of cloud properties around transitions. We find that LWP and rain amounts increase in closed cells up to ~25 hours before transitions, followed by decreasing <em>N_d</em> and increasing <em>r_e</em>, while cloud vertical structure remains largely unchanged. These findings support a precipitation-linked transition pathway, potentially triggered by enhanced boundary-layer moisture and amplified by aerosol scavenging&ndash;rain feedback. This new observational evidence advances our understanding of stratocumulus breakup with implications for the cooling effect of these clouds.