Radio signals transmitted by Global Navigation Satellite System (GNSS) satellites experience tropospheric delays. While the hydrostatic part, referred to as zenith hydrostatic delay (ZHD) when mapped to the zenith direction, can be analytically modelled with sufficient accuracy, the wet part, referred to as zenith wet delay (ZWD), is much more difficult to determine and needs to be estimated. Thus, there exist several ZWD models which are used for various applications such as positioning and climate research. In this study, we present a data-driven, global model of the spatial ZWD field, based on the Extreme Gradient Boosting (XGBoost). The model takes the geographical location, the time, and a number of meteorological variables (in particular, specific humidity at several pressure levels) as input, and can predict ZWD anywhere on Earth as long as the input features are available. It was trained on ZWDs at 10718 GNSS stations and tested on ZWDs at 2684 GNSS stations for the year 2019. Across all test stations and all observations, the trained model achieved a mean absolute error of 6.1 mm, respectively, a root mean squared error of 8.1 mm. Comparisons of the XGBoost-based ZWD predictions with independently computed ZWDs and baseline models underline the good performance of the proposed model. Moreover, we analysed regional and monthly models, as well as the seasonal behaviour of the ZWD predictions in different climate zones, and found that the global model exhibits a high predictive skill in all regions and across all months of the year.

全球导航卫星系统 (GNSS) 卫星传输的无线电信号会出现对流层延迟。虽然流体静力部分（映射到天顶方向时称为天顶静水延迟 (ZHD)）可以以足够的精度进行分析建模，但湿部分（称为天顶湿延迟 (ZWD)）更难以确定和确定。需要估计。因此，存在多种 ZWD 模型，可用于定位和气候研究等各种应用。在这项研究中，我们提出了一个基于极端梯度提升（XGBoost）的数据驱动的空间 ZWD 场全局模型。该模型以地理位置、时间和一些气象变量（特别是几个压力水平下的比湿度）作为输入，只要输入特征可用，就可以预测地球上任何地方的 ZWD。 2019 年，它在 10718 个 GNSS 站的 ZWD 上进行了训练，并在 2684 个 GNSS 站的 ZWD 上进行了测试。在所有测试站和所有观测中，训练模型的平均绝对误差分别为 6.1 毫米，均方根误差为8.1 毫米。基于 XGBoost 的 ZWD 预测与独立计算的 ZWD 和基线模型的比较强调了所提出模型的良好性能。此外，我们分析了区域和月度模型，以及 ZWD 预测在不同气候区的季节性行为，发现全球模型在所有区域和一年中的所有月份都表现出很高的预测能力。