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An algorithm is introduced to downscale the 0.6 and 0.8 μm spectral channels of the METEOSTAT SEVIRI satellite imager from 3×3 km<sup>2</sup> (LRES) to 1×1 km<sup>2</sup> (HRES) resolution utilizing SEVIRI's high-resolution visible channel (HRV). Intermediate steps include the coregistration of low- and high-resolution images, lowpass filtering of the HRV channel with the spatial response function of the narrowband channels, and the estimation of a least-squares linear regression model for linking high-frequency variations in the HRV and narrowband images. The importance of accounting for the sensor spatial response function for matching reflectances at different spatial resolutions is demonstrated, and an estimate of the accuracy of the downscaled reflectances is provided. Based on a 1-year dataset of Meteosat SEVIRI images, it is estimated that on average, the reflectance of a HRES pixel differs from that of an enclosing LRES pixel by standard deviations of 0.049 and 0.052 in the 0.6 and 0.8 μm channels, respectively. By applying our downscaling algorithm, explained variance of 98.2 and 95.3 percent are achieved for estimating these deviations, corresponding to residual standard deviations of only 0.007 and 0.011 for the respective channels. For this dataset, a minor misregistration of the HRV channel relative to the narrowband channels of 0.36±0.11 km in East and 0.06±0.10 km in South direction is observed and corrected for, which should be negligible for most applications.