Widespread extreme climate events cause many fatalities, economic losses and have a huge impact on critical infrastructure. It is therefore of utmost importance to estimate the frequency and associated consequences of spatially concurrent extremes. Impact studies of climate extremes are severely hampered by the lack of extreme observations, and even large ensembles of climate simulations often do not include enough extreme or record-breaking climate events for robust analysis. On the other hand, weather generators specifically fitted to extreme observations can quickly generate many physically or statistically plausible extreme events, even with intensities that have never been observed before. We propose a Fourier-based algorithm for generating high-resolution synthetic datasets of rare events, using essential concepts of classical modelling of (spatial) extremes. Here, the key feature is that the stochastically generated datasets have the same spatial dependence as the observed extreme events. Using high-resolution gridded precipitation and temperature datasets, we show that the new algorithm produces realistic spatial patterns, and is particularly attractive compared to other existing methods for spatial extremes. It is exceptionally fast, easy to implement, scalable to high dimensions and, in principle, applicable for any spatial resolution. We generated datasets with 10,000 gridpoints, a number that can be increased without difficulty. Since current impact models often require high-resolution climate inputs, the new algorithm is particularly useful for improved impact and vulnerability assessment.

中文翻译：

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快速生成高维空间极值


广泛的极端气候事件造成许多人死亡、经济损失，并对关键基础设施产生巨大影响。因此，估计空间并发极值的频率和相关后果至关重要。由于缺乏极端观测，极端气候的影响研究受到严重阻碍，即使是大型气候模拟集合也往往不包括足够的极端或破纪录的气候事件进行稳健分析。另一方面，专门适用于极端观测的天气发生器可以快速生成许多物理或统计上合理的极端事件，即使其强度是以前从未观测过的。我们提出了一种基于傅里叶的算法，用于使用经典极端事件建模的基本概念生成稀有事件的高分辨率合成数据集。在这里，关键特征是随机生成的数据集与观察到的极端事件具有相同的空间依赖性。使用高分辨率网格化降水和温度数据集，我们表明新算法可以产生逼真的空间模式，与其他现有的空间极端方法相比，它特别有吸引力。它非常快速、易于实施、可扩展至高维度，原则上适用于任何空间分辨率。我们生成了具有 10,000 个网格点的数据集，这个数字可以毫不费力地增加。由于当前的影响模型通常需要高分辨率的气候输入，因此新算法对于改进影响和脆弱性评估特别有用。