  We compare probabilistic predictions of extreme temperature anomalies issued
by two different forecast schemes. One is a dynamical physical weather model,
the other a simple data model. We recall the concept of skill scores in order
to assess the performance of these two different predictors. Although the
result confirms the expectation that the (computationally expensive) weather
model outperforms the simple data model, the performance of the latter is
surprisingly good. More specifically, for some parameter range, it is even
better than the uncalibrated weather model. Since probabilistic predictions are
not easily interpreted by the end user, we convert them into deterministic
yes/no statements and measure the performance of these by ROC statistics.
Scored in this way, conclusions about model performance partly change, which
illustrates that predictive power depends on how it is quantified.
