Something like Mahalanobis distance when the copula is not Gaussian Mahalanobis distance accounts for different variances of the marginal variables and correlations between the marginal variables. However, there is an implicit (maybe explicit) assumption that correlation is the right measure of dependence between the marginal variables. Certainly this need not be the case. We could be dealing with a very different type of copula than Gaussian, in which case, we can calculate Mahalanobis distance, but it isn't so meaningful.
Are there techniques for finding meaningful distances when the copula is not Gaussian? I'd really love it if the technique turned out to be Mahalanobis distance in the case of the Gaussian copula. An idea that I had involves the principal curvatures of the copula (eigenvalues of something?), though I haven't a clue what to do with that, and assuming differentiability becomes problematic when we deal with empirical distributions. (Maybe the estimate is a very different discussion.) Google Scholar seems not to return anything about doing differential geometry on the copula, however.
I think what I want to do is find confidence regions that need not be elliptical, though I am not 100% certain what I want to do with the distance measure.
 A: The Mahalanobis distance is a special case of the "null deviance"
Consider a random vector $\mathbf{X} \sim \text{N}(\boldsymbol{\mu}, \mathbf{\Sigma})$ with dimension $k$ and the Mahalanobis distance function:
$$D^2(\mathbf{x}) = (\mathbf{x} - \boldsymbol{\mu})^\text{T} \mathbf{\Sigma}^{-1} (\mathbf{x} - \boldsymbol{\mu})
\quad \quad \quad \text{for all } \mathbf{x} \in \mathbb{R}^k.$$
We can write the log-density for this distribution as:
$$\begin{align}
\log p (\mathbf{x}|\boldsymbol{\mu}, \mathbf{\Sigma})
&= -\frac{k}{2} \log(2 \pi) - \frac{1}{2} \log \det \mathbf{\Sigma} - \frac{1}{2} (\mathbf{x} - \boldsymbol{\mu})^\text{T} \mathbf{\Sigma}^{-1} (\mathbf{x} - \boldsymbol{\mu}) \\[6pt]
&= -\frac{k}{2} \log(2 \pi) - \frac{1}{2} \log \det \mathbf{\Sigma} - \frac{1}{2} D^2(\mathbf{x}) \\[6pt]
&= \log p (\mathbf{x}|\mathbf{x}, \mathbf{\Sigma}) - \frac{1}{2} D^2(\mathbf{x}), \\[6pt]
\end{align}$$
Using this result and setting $\hat{\boldsymbol{\mu}}_0 = \boldsymbol{\mu}$ and $\hat{\boldsymbol{\mu}}_\text{S} = \mathbf{x}$ gives the alternative expression:
$$\begin{align}
D^2(\mathbf{x}) 
&= 2 \Big[ \log p (\mathbf{x}|\mathbf{x}, \mathbf{\Sigma}) - \log p (\mathbf{x}|\boldsymbol{\mu}, \mathbf{\Sigma}) \Big] \\[6pt]
&= 2 \Big[ \log p (\mathbf{x}|\hat{\boldsymbol{\mu}}_\text{S}, \mathbf{\Sigma}) - \log p (\mathbf{x}|\hat{\boldsymbol{\mu}}_0, \mathbf{\Sigma}) \Big]. \\[6pt]
\end{align}$$
Now, this latter expression is the "null deviance" that occurs when we compare a saturated model with a free mean parameter against the null model where the mean parameter is fixed (at the null value $\boldsymbol{\mu}$).  Thus, the Mahalanobis distance can be considered to be a special case of the null deviance for this general comparison.
Since Mahalanobis distance is a special case of the null deviation, to extend the former concept you can simply use the latter.  That is, to extend the concept to broader models and distributions, we need merely employ the null deviance for the same comparison (free mean versus fixed mean) in the broader model/distribution.  If you are working with a parametric model then you can obtain the null deviance through the appropriate optimisation of the log-likelihood.  If you are working non-parametrically, you will need to estimate the null deviance.  In either case, the null deviance (or its estimate) constitutes a generalisation of the Mahalanobis distance that can be applied more broadly to models where the random vector is not normally distributed.
A: The implicit (maybe explicit) assumption that correlation is the right measure of dependence in the case of the Gaussian copula (with normal magins, or else "normalized" margins, see e.g. the nonparanormal) is very much explicit. Indeed, the Mahalanobis distance corresponds to the log-likelihood of the data in this case.
So say you have two iid random vectors ${\bf X},{\bf Y} \sim N({\bf \mu},{\bf{\Sigma}})$, their Mahalanobis distance is
$$
{\rm Maha}({\bf X},{\bf Y}|{\bf S}) = ({\bf X}-{\bf Y})^{\top}{\bf S^{-1}}({\bf X}-{\bf Y}),
$$
where ${\bf S}$ is an estimate of ${\bf \Sigma}$ (if not known).
Interpreting ${\bf Y}$ as an estimator of ${\bf \mu}$ (an unbiased one in fact), this is the log-likelihood of ${\bf X}$ (negated and up to some factor that we can neglect).
Note that one can judge that it is more appropriate, in this case, to use an estimate ${\bf S}$ of $\mathbb{Var}({\bf X}-{\bf Y})=2{\bf \Sigma}$, i.e. to evaluate the log-likelihood of ${\bf X}-{\bf Y} \sim N({\bf 0},2{\bf \Sigma})$, but this turns out to be an equivalent distance.
Now, to answer your question, I think that what your are looking for is some similar meaning in the case of a different copula. A good first step might be to (to some extent) explicitly write your likelihood function, or that of $({\bf X}-{\bf Y})$, and look for that meaning.
And if you do not want to depend on distributional assumptions, then I guess you better begin by answering the question: "what distance do I find meaningful for my application?" rather than deriving a meaningful distance for a given distribution. As pointed out by steveo'america, the Mahalanobis distance (assuming that your data has finite second moment) might actually be what you are looking for...
Hope that helps.
