A Gaussian copula maps the uniform marginal probabilities of each variable to a standard normal distribution using the inverse cumulative distribution function of the standard normal distribution. This mapping allows the dependence structure between the variables to be captured using a correlation matrix, enabling the construction of a joint multivariate distribution from arbitrary marginal distributions.