By analysis of statistical characteristics and probability density distribution of extreme values of wind pressures on the surfaces of a typical low-rise building model and a typical high-rise building model, characteristics of the commonly used methods for estimating the extreme-values of wind pressure are discussed. The relationship between the parameters of the extreme value distribution of wind pressure and its observation length is then deduced based on the generalized extreme value theory and the independence of the observed extreme values. A new method for estimating the extreme values is developed by dividing the time history sample of the wind pressure into several subsamples. The extreme values of the wind pressure coefficients calculated with the present method and those with the commonly used methods are compared and the results indicate that the present method can estimate the extreme values of non-Gaussian wind pressure more accurately than the commonly used ones. 1. Introduction Wind pressure on building surfaces is a random process, and its probabilistic and statistical characteristics are some of the key points to wind engineering. In the 1960s, Davenport [1] introduced statistical concepts into wind engineering and assumed that the wind speed, wind angle, and wind pressure coefficients all satisfy the Gaussian distribution. Until now, many studies on analyzing and modeling of wind effects still accept that the random processes involved in wind pressure assume a Gaussian process. This concept is mainly used to facilitate the analysis of wind pressures; many Gaussian processes are known to exist. This assumption is effective when the overall effect of random wind pressure field on an area is considered. Peterka and Cermak [2] and Kareem [3] indicated that, in an area where the mean wind pressure coefficients are lower than ？0.25, the wind pressure is generally skewed. They found many spikes in the wind pressure history. These spikes are six times the root-mean-square value from the mean values. The probability of their occurrence is much greater than that predicted by the Gaussian distribution. After many studies on the wind pressure on buildings, Stathopoulos [4] also indicated the considerable skewness of wind pressure data in certain areas. Tieleman and Reinhold [5] and Holmes and Best [6] also reached a similar conclusion. As the building is within the lower part of the atmospheric boundary layer, which experiences high turbulence, and surrounding obstacles are present, the windward wall of the building suffers from a non-Gaussian
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