## 1 Introduction  
The wind effects on high-rise buildings are widely studied by many researchers [1, 2, 3, 4]. The study of extreme pressure coefficients is a focus of extreme wind effects for high-rise buildings. The extreme pressure coefficient for a high-rise building due to extreme local wind pressures on its surface is uncertain. The quantification of the uncertainty, i.e., the probabilistic characterization, for such a random variable is important for calibrating the design wind load for the codes. This usually includes the probabilistic distribution fitting practice for the extreme pressure coefficients obtained from numerous wind tunnel tests on the buildings or from the literature. For example, [PERSON] and [PERSON] [5, 6] attempted to fit the extreme pressure coefficient for low-rise buildings by using the Gumbel distribution. To estimate the distribution parameters, they applied the best linear unbiased estimation (BLUE) method proposed by [PERSON] [7]. It is an optimized method for prediction of peak pressure from wind tunnel model tests. Similar research has been performed by [PERSON] and [PERSON] [8] considering the generalized extreme value (GEV) distribution with positive shape parameters. [PERSON] and [PERSON] [9] studied the effects of different probability distributions of wind speed and extreme load coefficients on the extreme wind effects. This indicated that the use of the extreme Type III distribution for the extreme load coefficient had almost no influence on the wind effects. [PERSON] et al. [10] suggested that the probability distribution of the extreme value of wind pressure on the surface of low-rise buildings could be expressed using Gumbel distribution and GEV distribution. [PERSON] [11] found that the probability distribution of extreme wind pressure on long-span roofs could be expressed using the GEV distribution. The GEV distribution gave the best fit for the extreme peak data of pressure for wind-induced acceleration analysis [12]. However, rare examples can be found for the probabilistic characterization of the extreme pressure coefficients for high-rise buildings from the literature. To estimate the extreme wind pressures on the claddings of high-rise buildings from the wind tunnel test, one of the issues is that the measurements are usually different between point pressures and spatial averages. Due to the lack of full spatial and temporal correlations, the localized intense pressures (e.g., point pressures) are likely to be attenuated for multiple surfaces (e.g., spatial averages) [13]. Such an attenuation effect is captured by several building design codes. An example can be found in ASCE 7-10 [14] in the provisions for the design of components and cladding, indicating that the pressures decrease exponentially with area for the design pressure coefficients. To accurately account for the attenuation effect, [PERSON] [15] and [PERSON] [16] concluded that the equivalent time averaging method is good for computing wind loads on finite areas of structures. However, [PERSON] and [PERSON] [13] indicated that area-averaged pressure coefficients can be used to determine cladding and component loads. The study shows that as the subordinate area of the component increases, the total wind load effect will decrease. \"Load Code for the design of building Structure\" GB50009-2012 [17] gives the area reduction factor of the surface local shape factor for taking into account the area reduction effects for wind load effects. The local shape factor is the mean pressure coefficient with the reference height of its own. As for the area reduction of the extreme pressure coefficient, GB50009-2012 lacks a corresponding quantitative description of the reduction relationship. In addition to the attenuation effect of the varying area on the surface pressures of the buildings, pressures may also differ significantly as a function of the wind direction.