Existing demosaicking neural models typically follow the same design principles as those adopted by the rest image restoration tasks; yet one unique & often overlooked problem with demosaicking is it also suffers from the moiré artifact. This observation inspires us to examine the key reason that moiré happens only to demosaicking instead of other image restoration tasks. In this process, We identify the spectral inconsistency concealed in the input of demosaicking neural networks; our findings also clarify the failure of the traditional dense spatiospectral feature aggregation in mitigating spectral inconsistency. Based on the analysis, a new solution is proposed to address moiré while preserving fine image details. In particular, we decouple the traditionally used spatio-spectral feature aggregation into comprehensive spectral aggregation and local spatial aggregation. Throughout a diverse range of experiments with quantitative and qualitative results, our approach is shown capable of significantly reducing artifacts such as moiré and over-smoothness, as well as drastically boosting state-of-the-art performance without increasing computational cost.