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This was also a case relevant for the MISTRAL initiative (an EU project in which Italian partners and ECMWF have collaborated). In that project we have the "Italy flash flood use case" (see here and here), spearheaded by ECMWF, in which post-processing, of different types, is applied to the ECMWF ENSemble, and also a 2.2km Italy-centred limited area COSMO ensemble. The post-processed outputs are blended together, with lead-time-dependant weighting, to make the final product, which aims to give better probabilistic rainfall forecasts, in particular with a view to providing improved early warnings of flash flood risk (via the association with extreme short period rainfall). Users are encouraged to focus on the higher percentiles (or probabilities of exceeding high thresholds) in the MISTRAL products, to gauge the potential for localised extremes. However some of the plots below illustrate mainly also how the ensemble mean is handled in the raw model and post-processed output, with differences between the two (for a given system) indicating the nature of any bias-correction being applied on the model grid-scale.

Whole table corresponds to 6h rainfall totals for 06-12UTC 28th November 2020
Column 0: Verifying observations
Columns 1 to -5: Ensemble Mean fields for ECMWF IFS (runs from 00UTC and 12UTC) and COSMO 2.2km (runs from 21UTC only, nominally shown as 00UTC here). PP means post-processed (different approaches for the two systems), for total precipitation valid 06-12UTC 28th November 2020
Columns 6, 7: 98th percentiles from ecPoint and post-processed COSMO
Column 8:
Column 6: 98th Percentile for MISTRAL-style output (: 98th Percentile, a blended version of post-processed ENS (12UTC run) and post-processed COSMO (21UTC run same day)