Suppose $X$ and $Y$ are continuous random variables with a joint density function $f_{X,Y}$. Both $X$ and $Y$ are supported on $(0,1)$ and have continuous (can be assumed differentiable) and non-increasing densities $f_X$ and $f_Y$ respectively. $f_X(x)>0$ and $f_Y(x)>0$ for $x\in(0,1)$. Let $M = \min\{X,Y\}$. Is $f_M(t)$, the density of $M$, necessarily non-increasing?

The conclusion is straightforward when $X$ and $Y$ are independent but I do not see how to prove it in the presence of unknown dependence. I could not find a counterexample as well.

Here are a couple of approaches that I tried.

Clearly, the CDF of $M$ can be expressed as $$F_{M}(t) := \Pr(M\le t)=1 - \int_{t}^1\int_{t}^1f_{X,Y}(x,y)dxdy.$$ Therefore, $$f_M(t) = \int_t^1f_{X,Y}(t,y)dy+\int_t^1f_{X,Y}(x,t)dx$$ and the derivative is $$f'_M(t) = \int_t^1\frac{\partial f_{X,Y}(t,y)}{\partial t}dy+\int_t^1\frac{\partial f_{X,Y}(x,t)}{\partial t}dx - 2f_{X,Y}(t,t).$$ I do not see how one can use the monotonicity conditions on the marginal densities to make progress from here.

Alternatively, we could write $\Pr(M\le t)$ as $$\Pr(M\le t) = \Pr(X\le t) +\Pr(Y\le t) - \Pr(\max\{X,Y\}\le t)$$ and consequently $$f_M(t) = f_X(t)+f_Y(t) - \frac{d}{dt}F_{X,Y}(t,t),$$ where $F_{X,Y}$ is the joint CDF. As far as I understand, for any $X$ and $Y$ we have a bivariate copula function $C(\cdot,\cdot)$ such that $F_{X,Y}(t,t) = C(F_X(t), F_Y(t))$ and therefore $$f'_M(t) = f'_X(t)(1-C_1)+f'_Y(t)(1-C_2)-(f^2_X(t)C_{11}+2f_X(t)f_Y(t)C_{12}+f^2_Y(t)C_{22}),$$ where $C_1$ is the derivative of $C(\cdot, \cdot)$ with respect to the first argument evaluated at $(F_X(t), F_Y(t))$ and $C_2, C_{11}, C_{12},$ and $C_{22}$ are defined analogously. The first two terms in the equation above are negative but it is not clear how to deal with the rest. I could not find any useful results in the copulas literature.