The analysis of trends in air temperature observations
is one of the most common activities in climate
change studies. This work examines the changes in daily
mean air temperature over Central Europe using quantile regression,
which allows the estimation of trends, not only in
the mean but in all parts of the data distribution. A bootstrap
procedure is applied for assessing uncertainty on the
derived slopes and the resulting distributions are summarised
via clustering. The results show considerable spatial diversity
over the central European region. A distinct behaviour
is found for lower (5 %) and upper (95 %) quantiles, with
higher trends around 0.15 C decade^{−1} at the 5 % quantile
and around 0.20 C decade^{−1} at the 95% quantile, the largest
trends (>0.2 C decade^{−1}) occurring in the Alps.grants E-83/09, HP2008-008 and SEJ2007-6450