A new type of extreme-value copulas has been developed by using conditional normal models. These models help in understanding complex relationships between variables by using a single hidden factor. The copulas are based on Gaussian distributions and can be used to analyze data with spatial or factor dependencies. The researchers have found special cases of these copulas and have created methods to estimate them accurately. By applying these models to wind and stock data, they have shown that they can effectively capture extreme values in these datasets.