Why correcting the distribution does not fix daily timing
Every stage lands daily Pearson r near 0.35 against CPC-UNI and near 0.24 against the independent BMKG stations. This is the expected behaviour of a marginal correction, not a shortcoming of this one. Each stage maps a pixel's value through a monotone non-decreasing function, so it introduces no rank reversals between days: if the satellite reported more rain on day i than day j, it still does after correction. Ordering is not preserved exactly, because dry-day matching ties about 43% of days at zero and ties can be created or broken there, but the effect on r is small. The day-by-day pairing is set by the satellite retrieval, and a marginal step cannot move rain from one day to its neighbour. You can reshape the distribution all you like - the timing does not move.
See it on a synthetic pixel
A synthetic satellite-and-gauge day series (illustrative, as in the thesis bound schematic). Drag the quantile-mapping strength: the corrected series slides onto the gauge distribution - the distribution mismatch (KS) collapses - but its correlation with the gauge barely twitches.
Correlation with gauge
Distribution mismatch (KS)
The same thing on the real numbers
Daily r by stage, against both references. The recipe is the same in structure: a median taken across the reference's own units within each dekad - over the 19,393 land pixels for CPC-UNI, over the stations for BMKG - then averaged across the 36 dekads. CPC-UNI is the dataset the correction was fitted to, so it is the in-sample number; the 172 BMKG stations are held out of the fitting. Both are flat across the stages, which is the point of the page.
The monthly Taylor
The limit is not an artefact of one summary statistic. Every product's Taylor position - all six, in all twelve months - sits at a similarly low correlation. The correction slides the cloud along the standard-deviation axis (LS under-spread → LSEQM/LSEQM+DL on the reference circle) but never toward the correlation axis. Each grey dot is one BMKG station (all six products pooled); the coloured dots are the per-product medians. The cloud concentrates in the low-correlation wedge - product medians between r = 0.20 and 0.24, most stations below 0.5 - so the limit is a property of the whole network, not of how the summary was taken. A thin tail of stations reaches higher, but no product median does. Pick a month: