Modern scientific technology such as particle accelerators, telescopes, and supercomputers are producing extremely large amounts of data. That scientific data needs to be processed by using systems with high computational capabilities such as supercomputers. Given that the scientific data is increasing in size at an exponential rate, storing and accessing the data are becoming expensive in both time and space. Most of this scientific data is stored by using floating point representation. Scientific applications executed on supercomputers spend a large amount of CPU cycles reading and writing floating point values, making data compression techniques an interesting way to increase computing efficiency. Given the accuracy requirements of scientific computing, we only focus on lossless data compression. In this paper we propose a masking technique that partially decreases the entropy of scientific datasets, allowing for a better compression ratio and higher throughput. We evaluate several data partitioning techniques for selective compression and compare these schemes with several existing compression strategies. Our approach shows up to 15% improvement in compression ratio while reducing the time spent in compression by half time in some cases.