That depends on the variable, scale and problem of interest. If you are interested in temperature, ERA-Interim is probably fine. For local precipitation or wind, you should really test how good a predictor ERA-Interim is. You may want to use other, maybe even stochastic, methods instead. Finally it is crucial whether you are interested in the local spatial dependence structure. Please have a look at my papers Maraun et al., Earth's Future, 2015; Maraun and Widmann, HESS, 2015; Maraun, J Climate, 2013.

We found no need to correct the bias of ERA-Interim data in central Italy (air temperature and precip). Anyhow, the classical methods used for correcting the bias of climate model projections (e.g., quantile mapping) can be applied also to ERA-Interim data.

That depends on the variable, scale and problem of interest. If you are interested in temperature, ERA-Interim is probably fine. For local precipitation or wind, you should really test how good a predictor ERA-Interim is. You may want to use other, maybe even stochastic, methods instead. Finally it is crucial whether you are interested in the local spatial dependence structure. Please have a look at my papers Maraun et al., Earth's Future, 2015; Maraun and Widmann, HESS, 2015; Maraun, J Climate, 2013.