## 1 Introduction  
Clouds play a vital role in the Earth's climate system, being one of its main drivers, while in turn, they are affected by climate changes. It has already been shown that evidence of climate change is apparent in the satellite cloud record [1]. However, there is still limited knowledge about how cloud, atmospheric circulation and climate interact [2], while disagreement exists about how clouds will change under future global warming [3]. The inaccuracy of cloud representation in global climate models is at present the main source of uncertainty in climate sensitivity estimates and climate change predictions [2]. Therefore, it is crucial to improve our capabilities to observe even basic cloud properties as, for instance, the Total Cloud Cover (TCC), in order to create long-term, uniform, and stable global databases. The rapid development and use of remote sensing techniques during the last decades have assisted the scientific community tremendously in producing numerous satellite cloud data records. For instance, various datasets have been developed from the Moderate Resolution Imaging Spectroradiometer (MODIS) measurements [4] aboard the National Aeronautics and Space Administration's (NASA) Aqua and Terra satellites, the Pathfinder Atmospheres Extended (PATMOS-x) [5] and The Satellite Application Facility on Climate Monitoring (CM SAF) Cloud, Albedo and Surface Radiation (CLARA) [6; 7] datasets, derived from measurements of the Advanced Very High Resolution Radiometer (AVHRR), mounted on the Polar Operational Environmental Satellites (POES) and Meteorological Operational Satellite Program of Europe (MetOp) platforms. An additional dataset is the International Satellite Cloud Climatology Project (ISCCP) [8; 9] which uses, besides the AVHRR, a variety of instruments onboard numerous geostationary satellites (including Meteosat, Geostationary Operational Environmental Satellite (GOES), and Geostationary Meteorological Satellite (GMS)). Nevertheless, an in-depth evaluation and intercomparison of the various cloud datasets is necessary, given the systematic errors or artifacts that arise from the different instruments used and retrieval algorithms applied in each satellite data record, which could mislead our understanding of the climatic role of clouds. Moreover, surface observations of TCC and cloud type, despite their problems and limitations (e.g. scarcity and inhomogeneity) [10; 11] have been performed since the second half of the 19 th century [12] and enable us to study long-term cloud trends. Such surface data should be included in the framework of comparisons among satellite cloud data. A substantial number of studies have focused on Europe or the Mediterranean since this particular region is considered a \"hot spot\" in the context of climate change [13; 14; 15]. [PERSON] et al. (2017) in [14] compared TCC from several satellite-based observational datasets (ISCCP-D2, CLARA-A1, PATMOS-x), surface observations (EECRA and ICOADS), reanalyses (ERA-Interim, MERRA), and CMIP5 simulations, and found an overall good agreement between the mean values of TCC of ISCCP, CLARA, PATMOS-x and surface observations over the Mediterranean. [PERSON] et al. (2004) [16] compared NOAA/AVHRR-derived TCC with surface observations for Europe and found pronounced latitudinal and seasonal variations and an overall good agreement with surface observations, except for the summer months during which the satellite-derived TCC was systematically lower than the surface observations. Other studies also validated short-term remote sensing products in specific European areas [17; 18; 19; 20; 21; 22; 23]. Since 2016, two state-of-the-art satellite cloud data sets have been released, namely CLARA-A2 [7] and ISCCP-H series [8], which are significant improvements to their previous versions, CLARA-A1 and ISCCP-D series. An intercomparison of these recent data sets as well as their evaluation, either on global or regional scales, is timely and will be useful for any research that includes these data sets.