  Proper scoring rules are an essential tool to assess the predictive
performance of probabilistic forecasts. However, propriety alone does not
ensure an informative characterization of predictive performance and it is
recommended to compare forecasts using multiple scoring rules. With that in
mind, interpretable scoring rules providing complementary information are
necessary. We formalize a framework based on aggregation and transformation to
build interpretable multivariate proper scoring rules.
Aggregation-and-transformation-based scoring rules are able to target specific
features of the probabilistic forecasts; which improves the characterization of
the predictive performance. This framework is illustrated through examples
taken from the literature and studied using numerical experiments showcasing
its benefits. In particular, it is shown that it can help bridge the gap
between proper scoring rules and spatial verification tools.
