With　the　high　volume　and　velocity　of　scientific　data　produced　on　high-performance　computing　systems,　it　has　become　increasingly　critical　to　improve　the　compression　performance.　Leveraging　the　general　tolerance　of　reduced　accuracy　in　applications,　lossy　compressors　can　achieve　much　higher　compression　ratios　with　a　user-prescribed　error　bound.　However,　they　are　still　far　from　satisfying　the　reduction　requirements　from　applications.　In　this　paper,　we　propose　and　evaluate　the　idea　that　data　need　to　be　preconditioned　prior　to　compression,　such　that　they　can　better　match　the　design　philosophies　of　a　compressor.　In　particular,　we　aim　to　identify　a　reduced　model　that　can　be　utilized　to　transform　the　original　data　to　a　more　compressible　form.　We　begin　with　a　case　study　of　Heat3d　as　a　proof　of　concept,　in　which　we　demonstrate　that　a　reduced　model　can　indeed　reside　in　the　full　model　output,　and　can　be　utilized　to　improve　compression　ratios.　We　further　explore　more　general　dimension　reduction　techniques　to　extract　the　reduced　model,　including　principal　component　analysis,　singular　value　decomposition,　and　discrete　wavelet　transform.　After　preconditioning,　the　reduced　model　in　conjunction　with　difference　between　the　reduced　model　and　full　model　is　stored,　which　results　in　higher　compression　ratios.　We　evaluate　the　reduced　models　on　nine　scientific　datasets,　and　the　results　show　the　effectiveness　of　our　approaches.