Pre-tactical contrail avoidance is a promising near-term strategy for reducing aviation’s non-CO₂ climate impacts, but its effectiveness depends strongly on the accuracy of upper-tropospheric humidity forecasts. Because persistent contrails only occur in ice-supersaturated air, even small humidity errors can shift regions between contrail-forming and contrail-free states. To quantify how this uncertainty affects predicted contrail formation and potential climate impact, we apply a Monte Carlo framework in which spatially correlated, time-varying humidity perturbations are calibrated to published cruise-altitude forecast-error statistics and propagated through the Contrail Cirrus Prediction model (CoCiP) using ERA-5 meteorology and observed flight trajectories. The resulting ensemble simulations show that CoCiP’s deterministic estimates can misrepresent both the magnitude and robustness of contrail climate impact, particularly near the relative-humidity threshold for persistent contrail formation. For high-impact warming flights, ensemble impacts are often lower than the deterministic estimate even when all ensemble members remain warming, reflecting the asymmetric response introduced by the saturation threshold. For cooling flights, deterministic estimates can likewise overstate the magnitude of expected cooling. At the fleet level, humidity uncertainty changes the ranking of flights by predicted climate impact and alters which flights remain strong candidates for mitigation under risk-based decision criteria. A simple altitude-modification example further shows how uncertainty-aware evaluation can identify trajectory changes that remain beneficial across plausible humidity realizations while preserving favorable cooling effects elsewhere. Overall, these results demonstrate that probabilistic impact estimates provide a more useful basis for contrail mitigation planning than deterministic predictions alone.