A quick test with a pair of random rasters is informative. (I love using random rasters because they are so useful for experimenting with and testing procedures.) The large ones I generated had means very close to the theoretical ideal of 1/2 and standard deviations very close to the ideal of 1/sqrt(12) for uniform distributions. Because these rasters were supposed to be independent, the principal components would be multiples of their sum and difference, suitably normalized.
After standardization (that is, converting them to their "z scores"), these two random rasters had means of zero, unit standard deviations, and extreme values close to sqrt(3) = 1.732051 in size. Their sums and differences should therefore have a mean of zero and SD of 1. Their extremes should be about sqrt(12/2) = sqrt(6) = 2.45 from the mean, for a total range of almost 4.90.
Instead, the two components of the output had means near 2.45, standard deviations of 1, and extremes from 0 to 4.87 (similar to the patterns shown in the question, but the question has larger means, maxima, and SDs). These incorrect means, correct SDs, correct ranges, and exactly zero minima strongly suggest that the bands have been additively shifted to make all their values nonnegative. (Asking for just one principal component does not change this: the additive shift still occurs.)
The workaround, then, is to subtract its mean from each of the PCA bands to shift it back to a zero mean. As a check, the standard deviation of each band should equal the square root of its eigenvalue reported in the PCA output file. In my test (because I computed only approximate z scores) the eigenvalues were 1.00047 and 0.99932. Sure enough, the standard deviations reported in the
Layer Properties dialog are 1.000234515307069 and 0.9996583939019468, respectively, and their squares agree perfectly with the eigenvalues (to the limited precision reported).
A quick hunt through the ArcGIS 10.1 help pages produced no information at all about the PCA output. If it cannot be found, we have to rely on tests like this to inform our understanding and hope we are correct.