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Creating data visualizations that are functional and aesthetically pleasing is important yet difficult. Here we ask whether creating visualizations using the painterly techniques of impressionist-era artists may help. In two experiments we rendered weather data from the Intergovernmental Panel on Climate Change into a common visualization style, glyph, and impressionism-inspired painting styles, sculptural, containment, and impasto. Experiment 1 tested participants' recognition memory for these visualizations and found that impasto, a style resembling paintings like Starry Night (1889) by Vincent van Gogh, was comparable with glyphs and superior to the other impressionist styles. Experiment 2 tested participants' ability to report the prevalence of the color blue (representative of a single weather condition) within each visualization, and here impasto was superior to glyphs and the other impressionist styles. Questionnaires administered at study completion revealed that styles participants liked had higher task performance relative to less liked styles. Incidental eye tracking in both studies also found impressionist visualizations elicited greater visual exploration than glyphs. These results offer a proof-of-concept that the painterly techniques of impressionism, and particularly those of the impasto style, can create visualizations that are functional, liked, and encourage visual exploration.