Observations of daily minimum and maximum land air temperatures, Tmin and Tmax, have traditionally been obtained through in situ observations at meteorological stations. While the station network is extensive, many land masses are poorly observed. Moreover, observations at stations are point observations and may not be representative of air temperatures at neighboring locations. Satellites provide the means to observe surface skin temperatures at spatial scales of tens of meters to kilometers. But although skin and near-surface air temperatures may be strongly coupled, the two quantities can differ by several degrees over land, where the magnitude of the difference is variable in both space and time. This study describes a method for estimating daily Tmin and Tmax at the pixel scale using geostationary satellite data, providing spatially detailed observations for areas unobserved in situ. A dynamic multiple linear regression model is developed using daily minimum and maximum land surface temperature (LSTmin and LSTmax), fraction of vegetation, distance from coast, latitude, urban fraction, and elevation as predictors. The method is demonstrated over Europe for 2012-2013; evaluation with collocated station observations indicates a mean satellite-minus-station bias of 0.0 to 0.5 degrees C with root-mean-square difference of 2.3 to 2.7 degrees C. The data derived here are not designed to replace traditional gridded station air temperature data sets, but to augment them. Satellite surface temperature data usually have larger uncertainties than in situ data sets, but they can offer spatial detail and coverage that the latter may not provide.
