Does applying quantile mapping to subsamples improve the bias correction of daily precipitation?

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An intercomparison of a large ensemble of statistical downscaling methods over Europe: Results from the VALUE perfect predictor cross‐validation experiment (2016, background): “However, we found that all implementations (even a daily moving window) resulted in a relevant performance improvement, differently to Reiter et al. (2017), where seasons were recommended for calibration.”
Robust bias-correction of precipitation extremes using a novel hybrid empirical quantile-mapping method (2021, result): “This example shows that the flexibility of EQM is also what makes it susceptible to overfitting on calibration data and supports other studies showing that EQM is sensitive to the choice of, and can overfit on calibration data (Reiter et al. 2018; Berg et al. 2012; Holthuijzen et al. 2021; Piani et ”