In testing a global null hypothesis, with independent tests, the p-values are distributed as $U(0,1)$. There are many goodness-of-fit tests to check that the distribution is uniform. For example, this answer discusses the use of chi-squared, Kolmogorov-Smirnov, and several others.
However, in many articles about combining test results, I've seen the recommendation to use Fisher's combined test. Does it have any specific advantage for global null testing, compared to the more widely used tests of uniformity that I listed above?