Mesoscale convective systems (MCSs) are difficult to forecast due to their inherent‐60 unpredictability and development from scales that are subgrid in typical global models. Here the impacts of model representation of convection on MCS structure and downstream forecast evolution are examined using two configurations of the Met Office Unified Model: the convection‐permitting (4.4‐km grid spacing) limited‐area Euro4 and convection‐parametrizing (25‐km grid spacing) Global configurations.MCSs are associated with a characteristic potential vorticity (PV) structure: a positive PV anomaly in the mid‐troposphere and negative PV anomalies above and to the side of it. Convection‐permitting models produce larger‐amplitude MCS PV anomalies than convection‐parametrizing models. These differences are shown to persist after coarse graining the output from a Euro4 simulation to the 25 km grid spacing of the Global configuration for a case study from July 2012, and are largest in magnitude and extent in the upper troposphere.The effect of the poor representation of this PV structure by convection‐ parametrizing models on forecasts is investigated by adding “MCS perturbations”, calculated as differences between the coarse‐grained Euro4 and the Global outputs, to five‐day Global configuration forecasts. Upper‐level MCS perturbations lead to greater forecast differences than those at middle levels, though using perturbations at all levels yields the greatest impact. For the first 36 hr, differences grow on the convective scale related to the MCS and its influence on a developing UK cyclone, despite perturbation amplitudes initially reducing. Subsequently, differences grow rapidly onto the synoptic scale and by five days impact the entire Northern Hemisphere. MCS perturbations slow the eastward movement of Rossby waves due to ridge amplification. Thus, perturbing convection‐parametrizing models to include PV anomalies associated with MCSs produces synoptic‐scale forecast differences implying that the misrepresentation of the PV structures associated with MCSs are a potential source of forecast errors.