Home-field advantage (HFA) predicts that litter decomposes faster under its derived plant species. Growing evidence suggests that HFA depends on litter chemical traits and the microbiome's composition, but whether and how their interactions influence HFA effects has rarely been explored. Here, we conducted a reciprocal field transplantation experiment in a temperate forest to examine the effects of litter quality and microbial community composition on decomposition rates. We collected leaf litter from four dominant tree species (Pinus koraiensis, Tilla amurensis, Fraxinus mandshurica, and Acer mono) and utilized two mesh sizes (i.e., 0.5-mu m and 35-mu m) to manipulate the composition of decomposer communities (i.e., small-sized bacteria only or bacteria + fungi). We assessed the effects of HFA and the ability of decomposers to decay a wide range of litter, i.e., the functional breadth (FB). We found that HFA was stronger for recalcitrant litter in the fine-mesh litterbags. Low litter phosphorus (P) and nitrogen (N) content, explained the positive HFA effects when only small-sized bacteria were present during litter decomposition, but not when both bacteria and fungi were present. Soil parameters such as potassium content and dissolved organic carbon also significantly affected decomposer communities and contributed to the variability in HFA effects. Finally, the FB negatively correlated with HFA in coarse-and fine-mesh litterbags and accounted for 46 and 54% of the observed variation in HFA effects, respectively. Our results highlight that the interactive relationships between litter quality, soil properties, and the microbiome's composition, are central in understanding the functional abilities of microbes during litter decomposition, and suggest a tradeoff between the microbiome's capacity to degrade all litter efficiently and the specialization of decomposers toward their own litter. This further underscores the independent role of decomposers communities in litter decomposition rates, which should be considered when predicting biogeochemical cycling in forest ecosystems.