  Many post-processing methods improve forecasts at individual locations but
remove their correlation structure, which is crucial for predicting
larger-scale events like total precipitation amount over areas such as river
catchments that are relevant for weather warnings and flood predictions. We
propose a method to reintroduce spatial correlation into a post-processed
forecast using an R-vine copula fitted to historical observations. This method
works similarly to related approaches like the Schaake shuffle and ensemble
copula coupling, i.e., by rearranging predictions at individual locations and
reintroducing spatial correlation while maintaining the post-processed marginal
distribution. Here, the copula measures how well an arrangement compares with
the historical distribution of precipitation. No close relationship is needed
between the post-processed marginal distributions and the spatial correlation
source. This is an advantage compared to Schaake shuffle and ensemble copula
coupling, which rely on a ranking with no ties at each considered location in
their source for spatial correlations. However, weather variables such as the
precipitation amount, whose distribution has an atom at zero, have rankings
with ties. To evaluate the proposed method, it is applied to a precipitation
forecast produced by a combination model with two input forecasts that deliver
calibrated marginal distributions but without spatial correlations. The
obtained results indicate that the calibration of the combination model carries
over to the output of the proposed model, i.e., the evaluation of area
predictions shows a similar improvement in forecast quality as the predictions
for individual locations. Additionally, the spatial correlation of the forecast
is evaluated with the help of object-based metrics, for which the proposed
model also shows an improvement compared to both input forecasts.
