Data sets of spatially irregular meteorological observations interpolated to a regular grid are not only important for climate analyses but are also essential in order to derive climatologies for rainfall-runoff models which require meteorological data sets as input forcing. For example, in the European Flood Awareness System (EFAS (Thielen et al., 2009; Bartholmes et al., 2009) long term observed meteorological data are used to drive the hydrological model LISFLOOD (van der Knijf, 2010) to obtain long term time series of simulated discharges at a pan-European scale. Those long term time series of simulated “proxy” discharges can then be used for statistical analysis, eg, to derive return periods or other time series derivatives.

There are several similar daily gridded data sets already available for Europe few of them contain the range of variables presented here or a similarly high spatial resolution. HadGHCND (Caesar et al., 2006) is a global gridded daily data set based upon near-surface maximum and minimum temperature observations. It spans the years 1946–2000 on a 2.5 latitude by 3.75 longitude grid. Furthermore, the JRC MARS-STAT database contains European meteorological observations interpolated onto a 50 km grid from 1975 up to present. Finally, the E-OBS gridded dataset covers the entire Europe with a grid resolution of 0.25 and spans a time from 1950 until 2012 (Haylock et al., 2008).