For most statistical post-processing schemes used to correct weather forecasts, changes to the forecast model induce a considerable reforcasting effort. We present a new approach based on response theory to cope with slight model change. In this framework, the model change is seen as a perturbation of the original forecast model. The response theory allows then to evaluate the variation induced on the averages involved in the statistical post-processing, provided that the magnitude of this perturbation is not too large. This approach is studied in the context of simple Ornstein-Uhlenbeck models, and then on a more realistic, yet simple, quasi-geostrophic model. The analytical results for the former case allow for posing the problem, while the application to the latter provide a proof-of-concept of the potential performances of response theory in a chaotic system. In both cases, the parameters of the statistical post-processing used -an Error-in-Variables Model Output Statistics (EVMOS)are appropriately corrected when facing a model change. The potential application in a more operational environment is also discussed.A generic property of the atmospheric dynamics is its sensivity to initial conditions. This implies that probabilistic forecasts will always be needed to adequately describe this behaviour (Wilks, 2011). Indeed, these methods represent a way to go beyond the natural predictability barrier that the chaotic atmospheric models exhibit (Vannitsem, 2017). These forecasts are at the same time subject to the impact of the presence of structural uncertainties, also known as model errors. Such errors degrade the forecasts as well, and their impact needs to be mitigated.Statistical post-processing methods are used to correct the operational predictions of the atmospheric models. An important family of statistical techniques used to post-process the forecasts are linear regression techniques, with possibly multiple predictors (Glahn and Lowry, 1972;Vannitsem and Nicolis, 2008), also known as Model Output Statistics (MOS). This rather simple but very efficient technique can be adapted to ensemble forecasts (e.g. Vannitsem (2009); Johnson and Bowler (2009); Glahn et al. (2009); Van Schaeybroeck and Vannitsem (2015)). One of the first approaches that was proposed is called Error-in-Variable MOS (EVMOS) because it takes into account the presence of errors in the observations and model observables (Vannitsem, 2009).Despite their simplicity, most post-processing schemes depend on the availability of a database of past forecasts, that allows one to "train" the regression algorithm by comparison with the observations database. These operational models are however subject to frequent evolution cycles, which are needed to improve their representation of the atmospheric behaviours. Therefore, there is a continuous need to recompute past forecasts with the latest model version, to avoid a degradation of the postprocessing schemes due to model change. Such recomputation of the past forecasts are called reforecasts, and typic...