In probability theory and statistics, the generalized extreme value (GEV) distribution is a family of continuous probability distributions developed within extreme value theory to combine the Gumbel, Fréchet and Weibull families also known as type I, II and III extreme value distributions. By the extreme value theorem the GEV distribution is the only possible limit distribution of properly normalized maxima of a sequence of independent and identically distributed random variables. Note that a limit distribution needs to exist, which requires regularity conditions on the tail of the distribution. Despite this, the GEV distribution is often used as an approximation to model the maxima of long (finite) sequences of random variables.

In some fields of application the generalized extreme value distribution is known as the Fisher–Tippett distribution, named after R.A. Fisher and L.H.C. Tippett who recognised three different forms outlined below. However usage of this name is sometimes restricted to mean the special case of the Gumbel distribution. The origin of the common functional form for all three distributions dates back to at least Jenkinson (1955), though allegedly it could also have been given by von Mises (1936).

Using the standardized variable ${\displaystyle s={\tfrac {x-\mu }{\sigma }}}$, where ${\displaystyle \mu }$, the location parameter, can be any real number, and ${\displaystyle \sigma >0}$ is the scale parameter; the cumulative distribution function of the GEV distribution is then

where ${\displaystyle \xi }$, the shape parameter, can be any real number. Thus, for ${\displaystyle \xi >0}$, the expression is valid for ${\displaystyle s>-{\tfrac {1}{\xi }}}$, while for ${\displaystyle \xi <0}$ it is valid for ${\displaystyle s<-{\tfrac {1}{\xi }}}$. In the first case, ${\displaystyle -{\tfrac {1}{\xi }}}$ is the negative, lower end-point, where ${\displaystyle F}$ is 0; in the second case, ${\displaystyle -{\tfrac {1}{\xi }}}$ is the positive, upper end-point, where ${\displaystyle F}$ is 1. For ${\displaystyle \xi =0}$, the second expression is formally undefined and is replaced with the first expression, which is the result of taking the limit of the second, as ${\displaystyle \xi \to 0}$ in which case ${\displaystyle s}$ can be any real number.

In the special case of ${\displaystyle x=\mu }$, we have ${\displaystyle s=0}$, so ${\displaystyle F(0;\xi )=\mathrm {e} ^{-1}\approx 0.368}$ regardless of the values of ${\displaystyle \xi }$ and ${\displaystyle \sigma }$.

The probability density function of the standardized distribution is

again valid for ${\displaystyle s>-{\tfrac {1}{\xi }}}$ in the case ${\displaystyle \xi >0}$, and for ${\displaystyle s<-{\tfrac {1}{\xi }}}$ in the case ${\displaystyle \xi <0}$. The density is zero outside of the relevant range. In the case ${\displaystyle \xi =0}$, the density is positive on the whole real line.

Since the cumulative distribution function is invertible, the quantile function for the GEV distribution has an explicit expression, namely