We introduce a new notion of positive dependence, namely multivariate total positivity of order two for conditional distributions, denoted cMTP$_2$, that explicitly incorporates the conditioning variables. This property is weaker than multivariate total positivity of order two for densities, but stronger than multivariate total positivity of order two for distribution functions, stochastic monotonicity, and tail monotonicity. It thus provides a natural intermediate concept linking these classical notions of positive dependence. We verify the cMTP$_2$ property for several distributions and copula families, including Archimedean copulas, and establish stability under Markov product transformations. As a consequence, we derive comparison results for measures of directed dependence arising from Markov structures, including Chatterjee's rank correlation and a related sensitivity measure.

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