  Increases in extreme weather events are a potentially important consequence
of anthropogenic climate change, yet, are difficult to attribute to ACC because
the record length is often similar to, or shorter than, extreme-event return
periods. This study is motivated by the ``World Weather Attribution Project''
and, their approach of fitting extreme value distribution functions to local
observations. The approach calculates the dependence of distribution parameters
on global mean surface temperature and uses this dependence to attribute
extreme events to ACC. Applying this method to preindustrial climate
simulations with no time-varying greenhouse gas forcing, we still find a strong
dependence of distribution parameters on GMST. This dependence results from
internal climate variability (e.g., ENSO) affecting both extreme events and
GMST. Therefore, dependence on GMST does not necessarily imply an effect of ACC
on extremes. For 3 different cases examined by WWA we find that the parametric
relationship between weather and GMST is indistinguishable between simulations
that are and are not forced by greenhouse gasses. We further consider for these
3 cases whether an extreme value, normal, or log-normal distribution better
represents the data; if a GMST-dependence of distribution parameters is
justified using a likelihood ratio test; and if a meaningful attribution is
possible given uncertainties in GMST dependence. We find that an attribution of
Australia's 2020--2021 Bushfires to ACC is difficult due to the effects of
Natural variability. For the 2019--2021 drought in Madagascar we find that the
small number of available data points precludes a meaningful attribution
analysis. Overall, we find that the effects of natural climate variability on
GMST and the uncertain relationship between GMST and regional extremes may lead
to inaccurate attribution conclusions using the WWA approach.
