I believe you're just referring to transforming each marginal distribution to $U[0,1]$ via the probability integral transform, which when applied to each of the variables individually, transforms a d-dimensional distribution to its copula.

For example, if you had a bivariate normal $(X,Y)$, and transform $U=F_X(X)$ and $V=F_Y(Y)$, then $(U,V)$ is a Gaussian copula.

e.g. see here

There are some recommended introductory readings here

I believe you're just referring to transforming each marginal distribution to $U[0,1]$ via the probability integral transform, which when applied to each of the variables individually, transforms a d-dimensional distribution to its copula.

For example, if you had a bivariate normal $(X,Y)$, and transform $U=F_X(X)$ and $V=F_Y(Y)$, then $(U,V)$ is a Gaussian copula.

e.g. see here

There are some recommended introductory readings here

I believe you're just referring to transforming each marginal distribution to $U[0,1]$ via the probability integral transform, which when applied to each of the variables individually, transforms a d-dimensional distribution to its copula.

For example, if you had a bivariate normal $(X,Y)$, and transform $U=F_X^{-1}(X)$ and $V=F_Y^{-1}(Y)$, then $(U,V)$ is a Gaussian copula.

e.g. see here

There are some recommended introductory readings here

I believe you're just referring to transforming each marginal distribution to $U[0,1]$ via the probability integral transform, which when applied to each of the variables individually, transforms a d-dimensional distribution to its copula.

For example, if you had a bivariate normal $(X,Y)$, and transform $U=F_X(X)$ and $V=F_Y(Y)$, then $(U,V)$ is a Gaussian copula.

e.g. see here

There are some recommended introductory readings here