Forecast busts – episodes of abnormally low forecast skill – remain a persistent challenge for numerical weather prediction despite steady improvements in forecasting skill. Previous studies have highlighted the roles of moist mesoscale processes and diabatically generated potential vorticity (PV) anomalies in triggering rapid error growth and downstream circulation misrepresentation, processes whose representation is highly sensitive to model grid spacing. In this study, we select three events that were classified as forecast busts in the Integrated Forecasting System (IFS) and investigate their sensitivity to horizontal grid spacing. The cases comprise (i) the explosive cyclogenesis of Storm Dennis (February 2020), (ii) a blocking event following the extratropical transition of Hurricane Franklin (September 2023), and (iii) a June 2020 event characterized by ridge amplification due to a warm conveyor belt (WCB) over the North Atlantic. For each case, we use IFS initial conditions and perform global ICON ( Icosahedral Non-hydrostatic model) ensemble forecasts with horizontal grid spacings ranging from 40 km to 2.5 km. We evaluate forecast skill against ERA5 reanalysis using anomaly correlation coefficients (ACC) of 500 hPa geopotential height. Across all cases, forecast busts are consistently linked to diabatically generated upper-level negative PV anomalies originating from strong latent heat release in organized convection, including mesoscale convective systems (MCSs), extratropical cyclones, and WCB ascent. These PV anomalies modify the upper-level waveguide, perturb the jet stream, and amplify downstream Rossby wave packets, degrading medium-range predictability. Decreasing horizontal grid spacing systematically improves the representation of diabatic processes, enhances the spatial extent and intensity of upper-level negative PV anomalies, and reduces wave-amplitude error growth. We observe that kilometer-scale simulations with horizontal grid spacing ≤5 km consistently yield the highest forecast skill, with substantial ACC improvements relative to coarser-resolution simulations. All three case studies systematically show that mesoscale diabatic processes are a primary source of ensemble spread and forecast error in bust situations, and that kilometer-scale global simulations significantly improve the representation of scale interactions governing Rossby wave amplification. These findings underscore the importance of kilometer-scale simulations for reliably representing scale interactions in strongly diabatic flow regimes. They are not only important for weather forecasting but also for climate simulations which feature long-standing deficiencies in capturing Rossby wave breaking and associate extreme weather events.