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The resolution mismatch between GCMs and in-situ gauge observations is an issue that has to be addressed for assessments and projections of precipitation extremes. The impacts of using different strategies to address this issue on GCM assessments and projections are evaluated in this study. The differences of precipitation extremes derived from GCMs at the original gridded resolutions and site-scale observations can be mostly explained by resolution mismatch. As the spatial and temporal "discontinuous" nature of precipitation, consecutive dry days (precipitation intensity) estimated from GCM data over a grid are likely to be shorter (smaller) than in-situ observations. By interpolating GCMs and observations to a common resolution, areal differences are moderately reduced, but spatial correlations between GCMs and observations may not be necessarily improved. By statistically downscaling the GCM-derived precipitation extremes, the indices agree better with the in-situ observations substantially. Using interpolation or downscaling to resolve resolution mismatch in GCMs may result in contradictory projected changes in extremes. Downscaled precipitation extremes generally change in greater magnitude than interpolated extremes in the projections. • Resolution mismatch explains the differences in precipitation extremes between the gridded GCMs and site-scale observations. • Interpolation partially reduces the differences, and downscaling largely improves the performance of GCM-based extremes. • Using interpolation or downscaling to resolve resolution mismatch may result in contradictory projected changes in extremes. [ABSTRACT FROM AUTHOR]