The need for long time series of gridded meteorological data with a fine spatial and temporal resolution has increased in recent years. The requirements for this type of gridded meteorological data fields arise from many different areas of the society, in connection to atmospheric environment studies of air quality and deposition and trends in these parameters, regional climate change, wind energy, hydrological studies etc. The aim of the present project is to investigate the possibility of producing historical, high quality and time consistent, meso-scale re analyses for the whole of Europe regarding precipitation, 2 m temperature and wind for at least 25 years back in time. The MESAN analysis system (Häggmark et al., 2000) at SMHI was chosen as a basis for the reanalysis and the system was adjusted to cover the whole of Europe. In order to find the most appropriate first guess fields to be used in the MESAN system, a pilot study was performed. ERA- 40 data from ECMWF was selected as best possible first guess fields for the re analysis. The performed re-analysis, which is denoted ERAMESAN, includes gridded data covering all Europe with a time resolution of 6 h and a spatial resolution of 0.1º (11 km) in a rotated latitude longitude coordinate system for the time-period 1980-2004. All analyses are archived in GRIB-format and stored on disc at SMHI. The dataset is also available within the EUMETNET optional programme Showcase EUROGRID. A partial validation for the years 1998-2000, using a cross validation procedure with independent observations (5.5% of the total amount of stations), shows an improvement in ERAMESAN compared to the ERA-40 data for all studied parameters with regard to root mean square deviation, mean absolute deviation and mean bias deviation for all seasons. The deviations are roughly of the order of 15% smaller compared to what is obtained from ERA-40. The frequency distribution of large precipitation amounts per day and high wind speeds are substantially better described in ERAMESAN compared to ERA-40. However, the tendency to underestimate the frequency of very large precipitation amounts or high wind speeds, compared to observations, can be seen also for ERAMESAN. It is important to be aware of this limitation when using ERAMESAN data for practical applications concerning evaluation of risks for extreme wind speeds or very large precipitation amounts or in e.g. wind energy studies.