To address the reduction in effective coincidence counts and the deterioration of imaging quality in entangled photon quantum imaging under rainfall scattering conditions, caused by raindrop extinction attenuation, multiple scattering deflection, and photon arrival-time mismatch, this article proposes a coincidence count optimization method for entangled optical quantum imaging in rainfall scattering channels. First, considering the complexity of signal photon propagation in rainfall environments and the limitation of conventional macroscopic attenuation models in accurately characterizing the contributions of multiple scattering paths, a quantum optical scattering and transmission attenuation model for rainfall channels is established by combining the raindrop size distribution spectrum with Mie scattering theory. On this basis, a multiple scattering photon tracing model based on weighted survival and path integral is formulated to calculate the spatial reception probability of signal photons under the constraints of field of the view and receiver aperture. Secondly, to account for the additional optical path induced by refraction when signal photons propagate through raindrops, the Cauchy-Crofton theorem is introduced to statistically correct the average propagation path inside raindrops. Based on this correction, the second-order correlation function is modified, and a joint detection probability model incorporating both spatial geometric constraints and temporal synchronization constraints is established. Finally, by further incorporating coincidence time window constraints as well as the statistical characteristics of detector dark counts and background noise, a pixel-level expected coincidence count expression is derived to realize target image reconstruction under rainfall conditions. Simulation and experimental results show that the proposed method can effectively suppress background noise under heavy rainfall conditions, enhance target structural preservation, and improve the quality of reconstructed images. Compared with traditional quantum imaging (QI) methods, the proposed method increases the peak signal-to-noise (PSNR) ratio by approximately 4.4 dB and reduces the root mean square error (RMSE) by about 40%, showing its effectiveness and robustness in complex rainfall scattering environments.