Multivariate extreme value models are used to investigate the combined behaviour of several weather variables. To investigate joint dependence of extreme rainfall events, a multivariate conditional modelling approach was considered to analyse the behaviour of joint extremes of rainfall events at selected weather stations in South Africa. Moreover, 1-day to 5-day indices of rainfall events were constructed to investigate the frequencies and intensities of rainfall events for selected weather stations. Then, the conditional multivariate modelling was fitted to investigate dependence between series of extreme rainfall events. The conditional multivariate modelling has provided all forms of dependence, using Laplace marginal transformations, for which all weather stations are not equally extreme. Bootstrap sampling was also employed to account for models uncertainty in computing the prediction standard errors and compared with the prediction obtained from the conditional modelling that was fitted to extreme data. The results obtained from predictions reflected both the marginal and the dependence features, as well as the extremal dependence structure described consistently for indices of rainfall events between weather stations. The modelling framework and results of this study contribute towards understanding the salient features on the extremal dependence of rainfall extremes which are associated with, e.g. flash floods and landslides. This knowledge has practical applications in disaster risk preparedness by communities.

多变量极值模型用于研究几个天气变量的组合行为。为了研究极端降雨事件的联合依赖性，考虑采用多元条件建模方法来分析南非选定气象站降雨事件的联合极端事件的行为。此外，还建立了1至5天的降雨事件指数，以调查选定气象站的降雨事件的频率和强度。然后，对条件多变量模型进行拟合，以研究一系列极端降雨事件之间的相关性。有条件的多元建模使用拉普拉斯边际变换提供了所有形式的依赖关系，对于这些依赖关系，所有气象站都并非同样极端。在计算预测标准误差时，还采用Bootstrap采样来说明模型的不确定性，并将其与从拟合极端数据的条件建模获得的预测进行比较。从预测中获得的结果既反映了边缘特征和依赖性特征，也反映了气象站之间降雨事件指数一致描述的极值依赖性结构。该研究的建模框架和结果有助于理解与极端降雨极端相关的显着特征，而极端极端降雨与暴雨和滑坡有关。这些知识在社区的灾害风险准备中具有实际应用。