Hydrological and climatic studies have always been constrained by the limited existing ground networks of raingauges. In this context, the benefit of the spatial technologies should be evident. However, these new sensors and/or products are not calibrated specifically for measuring such a parameter (rainfall), and the accuracy of the measurement is obviously dependent on the local context (topography, climatic context, etc.). In raingage-poor regions, global rainfall reanalysis products (output of meteorological models ran to reanalyze situations of the past) can constitute a valuable source of precipitation information for hydrological applications. Indeed, the spatial and time scales of reanalysis data sets are compatible with those of hydrological models. Our project aimed at developing specific regional correction algorithms, based on observed ground rainfall data and relevant regional parameters, to finally propose a non-biased rainfall spatial database, continuous, at the daily scale and for the whole African Continent. The ECMWF ERA-Interim reanalysis provides long rainfall time series from 1979. Over Africa, there is enough overlap with ground measurements to allow for calibrating an error-adjustment strategy. We present here the development of the climatic adjustment of the ERA-interim rainfall reanalysis dataset based on Tractebel historical raingauges dataset in the framework of a research and development partnership between Irstea and Tractebel. The approach consists in building a seasonal intensity-dependent error correction curve using all data points where ground measurements are available. As ground rainfall data is not available everywhere over the continent, and as most of the observed data are not recent ones, one correction curve is estimated for each region. The stationarity of the correction is tested for the available period of observation. Assumptions of the method are presented as well as regionalization results, as different regionalization approaches for error correction curves have been tested for extrapolation of bias correction to ungauged regions. Finally, the large rainfall data base is presented covering the whole Africa at daily scale, allowing several explorations and analyses usable for hydrological studies carried out for hydropower and dam projects. This rain fall database will also be extremely valuable for other types of study or project, such as for example Climate Change studies or Integrated Water Resources Management projects.