Nowadays, people are sharing their pictures on online social networks (OSNs), so OSN is a good platform for Steganography. But OSNs usually perform JPEG compression on the uploaded image, which will invalidate most of the existing steganography algorithms. Recently, some works try to design robust steganography which can resist JPEG compression, such as Dither Modulation-based robust Adaptive Steganography (DMAS) and Generalized dither Modulation-based robust Adaptive Steganography (GMAS). They relieve the problem that the receivers cannot extract the message correctly when the quality factor of channel JPEG compression is larger than that of cover images. However, they only can realize limited resistance to detection and compression due to robust domain selection. To overcome this problem, we meticulously explore three lossy operations in the JPEG recompression and discover that the key problem is spatial overflow. Then two preprocessing methods Overall Scaling (OS) and Specific Truncation (ST) are presented to remove overflow before message embedding as well as generate a reference image. The reference image is employed as the guidance to build asymmetric distortion for removing overflow during embedding. Experimental results show that the proposed methods significantly surpass GMAS in terms of security and achieve comparable robustness.