  Spatial verification of global high-resolution weather forecasts remains a
considerable challenge. Most existing spatial verification techniques either do
not properly account for the non-planar geometry of a global domain or their
computation complexity becomes too large. We present an adaptation of the
recently developed Precipitation Attribution Distance (PAD) metric, designed
for verifying precipitation, enabling its use on the Earth's spherical
geometry. PAD estimates the magnitude of location errors in the forecasts and
is related to the mathematical theory of Optimal Transport as it provides a
close upper bound for the Wasserstein distance. The method is fast and flexible
with time complexity $O(n \log(n))$. Its behavior is analyzed using a set of
idealized cases and 7 years of operational global high-resolution deterministic
6-hourly precipitation forecasts from the Integrated Forecasting System (IFS)
of the European Centre for Medium-Range Weather Forecasts. The summary results
for the whole period show how location errors in the IFS model grow steadily
with increasing lead time for all analyzed regions. Moreover, by examining the
time evolution of the results, we can determine the trends in the score's value
and identify the regions where there is a statistically significant improvement
(or worsening) of the forecast performance. The results can also be analyzed
separately for different intensities of precipitation. Overall, the PAD
provides meaningful results for estimating location errors in global
high-resolution precipitation forecasts at an affordable computational cost.
