Bayesian inversion framework for earthquake source processes yields an ensemble of plausible posterior models given prior information and data. Yet, the posterior and predictive analysis of source models and interlinked uncertainty remain underexplored. Here, we analyze inferred rupture properties and predictive observables of five Bayesian kinematic finite-fault models for large megathrust earthquakes: 2011 Mw 9.0 Tohoku, 2014 Mw 8.1 Iquique, 2015 Mw 8.3 Illapel, 2016 Mw 7.8 Pedernales, and 2015 Mw 7.8 Gorkha. Through selected models derived from joint datasets and consistent inversion schemes, we assess relationships between kinematic rupture parameters, including slip, rupture speed, rise time, and average slip rate, locally and within and across events. We use posterior model ensembles to compute static surface deformation and on-fault stress changes, dynamic ground motion, and their spatiotemporal covariances. For posteriors, patch-level correlations are robust between some parameters given expected tradeoffs, but intra-event correlations between most parameters are variable across events. The discrepancies with theoretical predictions imply limitations in adopted physics and/or inversion approaches for heterogeneous rupture scenarios. Our analysis reveals characteristic deformation and stress change patterns over the megathrust, with uncertainty influenced by station coverage, source characteristics, and data-model error structure. Despite large spatial variability of source properties, static elastic strain drop is tightly constrained within ~100–300 microstrain, while static stress drop spans ~5–25 MPa. Predicted peak ground displacements (PGD) exhibit near-field discrepancies with empirical scaling laws due to finite-source and directivity effects. Our findings inform physics-based earthquake modeling and improve quantification of rupture complexity and hazard impacts.