Rain　streaks　usually　result　in　severe　image　visual　degradation　and　foreground　occlusion,　affecting　the　quality　of　computer　tasks　in　outdoor　scenes.　Currently,　the　mainstream　methods　in　single-image　deraining　are　based　on　data-driven.　However,　the　deep　learning　network　could　be　imperfect,　with　limited　power　for　learning　the　global　information　from　rain　streaks　all　over　the　map.　In　order　to　solve　this　problem,　we　proposed　a　novel　Hierarchical　Distillation　Network　(HDNet).　In　this　network,　Hierarchical　Feature　Extraction　Block　(HFEB)　can　fully　utilize　the　Transformer＇s　learning　ability　in　high-level　features,　integrate　local　detail　extraction　and　global　structure　representation,　and　compensate　for　the　weakness　of　the　Convolutional　Neural　Network　(CNN),　which　is　overattentive　to　the　underlying　image　features.　Furthermore,　the　Distillation-Calibration　Block　(DCB)　are　adopted　to　avoid　feature　redundancy　during　model　training　and　calibrate　the　channel　and　spatial　information　through　the　feature　transmission,　which　could　significantly　improve　the　learning　efficiency.　Finally,　the　experiment　results　show　that　our　model　performs　better　than　traditional　CNN　models　and　state-of-the-art　methods.