A Gaussian copula constructs a multivariate distribution by mapping the marginal cumulative distribution function (CDF) of each variable to a standard normal distribution. This is done by applying the inverse of the standard normal CDF to the uniform probabilities obtained from the marginal distributions. Once mapped to standard normal variables, their joint dependence is modeled using a multivariate normal distribution with a specified correlation matrix.