Consider the following Metropolis-Hastings scheme to sample independent geometric random variables $X = (X_1, \dots, X_N)$, where each $X_j$ has pmf $\mathbb{P}(X_j = x) = p(1-p)^x$ for $x \geq 0$. At iteration $t$ we either propose (with probability $1/2$) an adding move, where for $j = 1,\dots, N$, $$x_{j,t}^\prime = \begin{cases} x_{j, t-1} + 1 & \text{with probability } 1/2,\\ x_{j, t-1}& \text{otherwise}. \end{cases}$$ or a removing move (with probability $1/2$), where for $j = 1,\dots, N$, $$x_{j,t}^\prime = \begin{cases} x_{j, t-1} - 1 & \text{with probability } 1/2 \text{ if $x_{j, t-1} > 0$},\\ x_{j, t-1}& \text{otherwise}. \end{cases}$$ The Metropolis-Hastings acceptance probability for an adding move is below, where $\delta(j)$ is a binary function that denotes whether we proposed an increase for RV $X_j$. $$\min \left\{1, \frac{1/2}{1/2} \prod_{j = 1}^N (1-p)^{\delta(j)} \frac{(1/2)^{1 - \delta(j)}(1/2)^{\delta(j)}}{(1/2)^{\delta(j)}(1/2)^{1 - \delta(j)}} \right\} = \min\left\{1, \prod_{j=1}^N (1-p)^{\delta(j)}\right\},$$ where the $(1-p)^{\delta(j)}$ term is the ratio of the target distribution, the first fraction is the ratio of the probabilities of proposing an adding move and a removing move, and the second fraction is the ratio of the proposal probabilities given a proposing or removing move. In the case where $\delta(j) = 0$ for all $j$ then the acceptance probability formula is different because $x_t = x_{t-1}$ and this could be the result of an adding or a removing move, but the acceptance probability still equals 1.
The probability of accepting a removing move is $$\min \left\{1, \frac{1/2}{1/2} \prod_{j = 1}^N \left[ (1-p)^{-\gamma(j)} \frac{(1/2)^{1 - \gamma(j)}(1/2)^{\gamma(j)}}{(1/2)^{\gamma(j)}(1/2)^{1 - \gamma(j)}}\right]^{\mathbb{I}\{x_{j,t-1} > 0\}} \right\} = \min\left\{1, \prod_{j=1}^N (1-p)^{-\gamma(j) \mathbb{I}\{x_{j,t-1} > 0\}}\right\},$$ where $\gamma(j)$ denotes whether we proposed a decrease for $X_j$.
If I run this with $N=1, p=1/2$ then I produce a sample from a geometric($1/2$) distribution, but with $N=2, p = 1/2$ I produce two independent samples from a geometric($5/8$) distribution. Any idea why?