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Summary:The increase of the Antarctic ice-sheet surface mass balance due to rise in snowfall is the only expected negative contribution to sea-level rise in the course of the 21st century within the context of global warming induced by mankind. Dynamical downscaling of climate projections provided by coupled ocean-atmosphere models is the most commonly used method to assess the future evolution of the Antarctic climate. Nevertheless, large uncertainties remain in the application of this method, particularly because of large biases in coupled models for oceanic surface conditions and atmospheric large-scale circulation at Southern Hemisphere high latitudes.In the first part of this work, different bias-correction methods for oceanic surface conditions have been evaluated. The results have allowed to select a quantile-quantile method for sea surface temperature and an analog method for sea-ice concentration. Because of the strong sensitivity of Antarctic surface climate to the variations of sea-ice extents in the Southern Ocean, oceanic surface conditions provided by two coupled models, NorESM1-M and MIROC-ESM, showing clearly different trends (respectively -14 and -45%) on winter sea-ice extent have been selected. Oceanic surface conditions of the ``business as usual" scenario (RCP8.5) coming from these two models have been corrected in order to force the global atmospheric model ARPEGE.In the following, ARPEGE has been used in a stretched-grid configuration, allowing to reach an horizontal resolution around 40 kilometers on Antarctica. For historical climate (1981-2010), the model was driven by observed oceanic surface conditions as well as by those from MIROC-ESM and NorESM1-M historical simulation. For late 21st century (2071-2100), original and bias corrected oceanic conditions from the latter two model have been used. The evaluation for present climate has evidenced excellent ARPEGE skills for surface climate and surface mass balance as well as large remaining errors on large-scale atmospheric circulation even when ...