To better manage the risks of destructive natural disasters, impact models can be fed with simulations of extreme scenarios to study the sensitivity to temporal and spatial variability. We propose a semi-parametric stochastic framework that enables simulations of realistic spatio-temporal extreme fields using a moderate number of observed extreme space-time episodes to generate an unlimited number of extreme scenarios of any magnitude. Our framework draws sound theoretical justification from extreme value theory, building on generalized Pareto limit processes arising as limits for event magnitudes exceeding a high threshold. Specifically, we exploit asymptotic stability properties by decomposing extreme event episodes into a scalar magnitude variable (that is resampled), and an empirical profile process representing space-time variability. For illustration on hourly gridded precipitation data in Mediterranean France, we calculate various risk measures using extreme event simulations for yet unobserved magnitudes, and we highlight contrasted behavior for different definitions of the magnitude variable.

为了更好地管理破坏性自然灾害的风险，可以将影响模型与极端情景的模拟结合起来，以研究对时间和空间变异性的敏感性。我们提出了一个半参数随机框架，该框架可以使用中等数量的观察到的极端时空事件来模拟真实的时空极端场，以生成无限数量的任意大小的极端情况。我们的框架从极值理论中汲取了合理的理论依据，建立在广义Pareto极限过程的基础上，该极限过程是由于事件幅度超过高阈值而引起的。具体来说，我们通过将极端事件事件分解为标量大小变量（重新采样）以及表示时空可变性的经验剖面过程来利用渐近稳定性。