Abstract. The Antarctic continent is a vast desert and is the coldest and the most unknown area on Earth. It contains the Antarctic ice sheet, the largest continental water reservoir on Earth that could be affected by the current global warming, leading to sea level rise. The only significant supply of ice is through precipitation, which can be observed from the surface and from space. Remote-sensing observations of the coastal regions and the inner continent using CloudSat radar give an estimated rate of snowfall but with uncertainties twice as large as each single measured value, whereas climate models give a range from half to twice the space–time-averaged observations. The aim of this study is the evaluation of the vertical precipitation rate profiles of CloudSat radar by comparison with two surface-based micro-rain radars (MRRs), located at the coastal French Dumont d'Urville station and at the Belgian Princess Elisabeth station located in the Dronning Maud Land escarpment zone. This in turn leads to a better understanding and reassessment of CloudSat uncertainties. We compared a total of four precipitation events, two per station, when CloudSat overpassed within 10 km of the station and we compared these two different datasets at each vertical level. The correlation between both datasets is near-perfect, even though climatic and geographic conditions are different for the two stations. Using different CloudSat and MRR vertical levels, we obtain 10 km space-scale and short-timescale (a few seconds) CloudSat uncertainties from −13 % up to +22 %. This confirms the robustness of the CloudSat retrievals of snowfall over Antarctica above the blind zone and justifies further analyses of this dataset.

Abstract. CMIP5, CMIP6, and ERA5 Antarctic precipitation is evaluated against CloudSat data. At continental and regional scales, ERA5 and the median CMIP models are biased high, with insignificant improvement from CMIP5 to CMIP6. However, there are fewer positive outliers in CMIP6. AMIP configurations perform better than the coupled ones, and, surprisingly, relative errors in areas of complex topography are higher (up to 50 %) in the five higher-resolution models. The seasonal cycle is reproduced well by the median of the CMIP models, but not by ERA5. Progress from CMIP5 to CMIP6 being limited, there is still room for improvement.