  For most statistical postprocessing schemes used to correct weather
forecasts, changes to the forecast model induce a considerable reforecasting
effort. We present a new approach based on response theory to cope with slight
model changes. In this framework, the model change is seen as a perturbation of
the original forecast model. The response theory allows us then to evaluate the
variation induced on the parameters involved in the statistical postprocessing,
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 help to pose the problem, while the application to
the latter provide a proof-of-concept and assesses the potential performances
of response theory in a chaotic system. In both cases, the parameters of the
statistical postprocessing used - an Error-in-Variables Model Output Statistics
(EVMOS) - are appropriately corrected when facing a model change. The potential
application in an operational environment is also discussed.
