  The rise in global mean temperature is an incomplete description of warming.
For many purposes, including agriculture and human life, temperature extremes
may be more important than temperature means and changes in local extremes may
be more important than mean global changes. We define a nonparametric statistic
to describe extreme temperature behavior by quantifying the frequency of local
daily all-time highs and lows, normalized by their frequency in the null
hypothesis of no climate change. We average this metric over 1218 weather
stations in the 48 contiguous United States, and find significantly fewer
all-time lows than for the null hypothesis of unchanging climate. Record highs,
by contrast, exhibit no significant trend. The metric is evaluated by Monte
Carlo simulation for stationary and warming temperature distributions,
permitting comparison of the statistics of historic temperature records with
those of modeled behavior.
