  We provide a quantitative analysis of the phenomenon of crowding of
near-extreme events by computing exactly the density of states (DOS) near the
maximum of a set of independent and identically distributed random variables.
We show that the mean DOS converges to three different limiting forms depending
on whether the tail of the distribution of the random variables decays slower
than, faster than, or as a pure exponential function. We argue that some of
these results would remain valid even for certain {\em correlated} cases and
verify it for power-law correlated stationary Gaussian sequences. Satisfactory
agreement is found between the near-maximum crowding in the summer temperature
reconstruction data of western Siberia and the theoretical prediction.
