  In recent environmental studies extreme events have a great impact. The
yearly and monthly maxima of environment related indices can be analysed by the
tools of extreme value theory. For instance, the monthly maxima of the fire
weather index in British Columbian forests might be modelled by GEV
distribution, but the stationarity of the time series is questionable. This
property can lead us to different approaches to test if there is a significant
trend in past few years data or not. An approach is a likelihood ratio based
procedure which has favourable asymptotic properties, but for realistic sample
sizes it might have a large error. In this paper we analyse the properties of
the likelihood ratio test for extremes by bootstrap simulations and aim to
determine a minimal required sample size. With the theoretical results we
re-asses the trends of fire weather index in British Columbian forests.
