  Probabilistic weather forecasts from ensemble systems require statistical
postprocessing to yield calibrated and sharp predictive distributions. This
paper presents an area-covering postprocessing method for ensemble
precipitation predictions. We rely on the ensemble model output statistics
(EMOS) approach, which generates probabilistic forecasts with a parametric
distribution whose parameters depend on (statistics of) the ensemble
prediction. A case study with daily precipitation predictions across
Switzerland highlights that postprocessing at observation locations indeed
improves high-resolution ensemble forecasts, with 4.5% CRPS reduction on
average in the case of a lead time of 1 day. Our main aim is to achieve such an
improvement without binding the model to stations, by leveraging topographical
covariates. Specifically, regression coefficients are estimated by weighting
the training data in relation to the topographical similarity between their
station of origin and the prediction location. In our case study, this approach
is found to reproduce the performance of the local model without using local
historical data for calibration. We further identify that one key difficulty is
that postprocessing often degrades the performance of the ensemble forecast
during summer and early autumn. To mitigate, we additionally estimate on the
training set whether postprocessing at a specific location is expected to
improve the prediction. If not, the direct model output is used. This extension
reduces the CRPS of the topographical model by up to another 1.7% on average at
the price of a slight degradation in calibration. In this case, the highest
improvement is achieved for a lead time of 4 days.
