## 1 Introduction  
The accelerated process of urbanization is faced with the problems of limited land resources and business needs in economic development, thus contributing to a large extent to the growth of demand for high-rise buildings, which is a response to the needs of the urban economy, as well as social development [1, 2]. As global climate change intensifies, the design and construction of high-rise buildings increasingly need to take into account the impacts of extreme weather events, especially changes in wind speed, which directly influence the structural safety and comfort of high-rise buildings [3, 4]. As the capital city of China and a densely populated city, the near-surface wind field characteristics of Beijing change significantly with the increasing density of urban structures [5]. In addition, due to the strong randomness of natural winds, it is very difficult to develop a universally applicable wind load design code based only on topographic coefficients and building characteristics for the wind-resistant design of buildings [6; 7]. Wind field characteristics are the basis for the study of wind loads and provide the basic information that influences the magnitude and distribution of wind loads [8; 9]. In this context, long-term, actual wind speed observations across multiple altitude levels and studies of measured wind field characteristics are essential. Wind load is the actual wind force acting on a building or structure, which is determined by a combination of varying wind speeds and directions, as well as the shape, size, and surface roughness of the building [10; 11; 12]. In the calculation of wind loads, on the one hand, the dynamic response of the structure, including the vibrations and deformations caused by wind, as well as the architectural features, is primarily addressed through a modal analysis and computational fluid dynamics (CFD) [13; 14; 15]. On the other hand, the calculation of wind loads is based on the determination of basic wind pressure, which is a theoretical value derived from wind field characteristics according to [PERSON]'s equation and can be calculated from basic wind speed, air density, and dimensionless coefficients [16; 17]. In architecture and engineering design, basic wind speed, defined as the speed not to be exceeded during a specific return period (usually 50 or 100 years), is used as a reference value for the calculation of wind loads [18; 19]. Extreme value models allow us to estimate the maximum wind speed over these long return periods, thereby determining the basic wind speed [20; 21]. However, due to the stochastic nature of wind speeds and limited wind speed samples, it is difficult to determine the most universally suitable probability distribution model, and many studies have been conducted to compare different distribution models and evaluate the most suitable one through parametric tests [22; 23; 24]. In order to improve the performance of the models, many scholars have paid attention to joint probability distributions, such as the joint probability distributions of wind speeds at two different locations in space or time, or the study of wind speeds in combination with covariates such as wind direction and the wind shear index [25; 26]. In the study of extreme value models, the traditional smooth model assumes that the data are smooth in time and that their statistical properties do not vary over time [27]. However, under the effect of factors such as climate change and urbanization, the characteristics of extreme wind speeds may change due to long-term trends, cyclical changes in wind speeds, etc., at which time the distribution model of wind speeds may also change [28; 29]. Non-stationary extreme value models allow for the probability distribution of extremes, such as maxima or minima, to vary over time, environmental conditions, or other influencing factors. These models typically require more complex mathematical and statistical methods for estimation and validation, including a time-series analysis and nonlinear regression models [30; 31; 32].