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Because of the ever-increasing execution scale of scientific applications, how to store the extremely large volume of data efficiently is becoming a serious issue. A significant reduction of the scientific data size can effectively mitigate the I/O burden and save considerable storage space. Since lossless compressors suffer from limited compression ratios, error-controlled lossy compressors have been studied for years. Existing error-controlled lossy compressors, however, focus mainly on absolute error bounds, which cannot meet users' diverse demands such as pointwise relative error bounds. Although some of the state-of-the-art lossy compressors support pointwise relative error bound, the compression ratios are generally low because of the limitation in their designs and possible spiky data changes in local data regions. In this work, we propose a novel, efficient approach to perform compression based on the …