Sensitivity of goodness-of-fit statistics to rainfall data rounding off

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Threshold detection for the generalized Pareto distribution: Review of representative methods and application to the NOAA NCDC daily rainfall database (2016, methodology): “…to as peaks over threshold, POT) [see e.g., Davison and Smith, 1990; Wang, 1991; Rosbjerg and Madsen, 1992, 1995; Madsen et al., 1997a, 1997b, 2002; Lang et al., 1999; Willems, 2000; Deidda and Puliga, 2006; Cooley et al., 2007; Veneziano et al., 2007; Deidda, 2010; Serinaldi and Kilsby, 2014].”
Automated threshold selection for extreme value analysis via ordered goodness-of-fit tests with adjustment for false discovery rate (2018, methodology): “It has been studied in detail [Deidda and Puliga (2006, 2009), Langousis et al. (2016)].”
Performances of some parameter estimators of the generalized Pareto distribution over rounded-off samples (2009, methodology): “Deidda and Puliga (2006) suggested computing the regions of acceptance of the goodness of fit tests on roundedoff samples generated via Monte Carlo techniques, adopting the same rounding-off rule as the observed sample.”
A multiple threshold method for fitting the generalized Pareto distribution to rainfall time series (2010, background): “Finally, the detection of an optimum threshold becomes even more difficult, if not impossible using available methods, on heavily quantized records (Deidda and Puliga, 2006).”