The objectives of this study were to explore the features and distribution pattern of litter mass (LM) and litter depth (LD) in montane forests across the Loess Plateau region, Northern China, and to reveal the main potential environmental factors influencing LM and LD features through regression analysis. LM and LD data were obtained through systematic sampling. The differences in LM and LD with regard to forest types were analyzed using the Kruskal-Wallis rank sum test. The factors influencing LM and LD distributions were statistically examined by using a linear mixed-effects model. In addition, the correlation between LM and LD was analyzed using an ordinary least-squares regression model. The following results were obtained. 1) The LM and LD varied markedly among different forest types. The LM was significantly heavier and the LD significantly thicker in both conifer forest (CF) and conifer and broad-leaved mixed forest (CBMF) than in broad-leaved forest (BF). However, no significant difference in both LM and LD was detected between CF and CBMF. 2) Both LM and LD appeared in a unimodal pattern along the latitudinal gradient, peaking at 35°-36°N and 36°-37°N, respectively. 3) There was no significant pattern in LM distribution along an altitudinal gradient, whereas LM decreased with rising elevation; the exceptions being several sites over 3000 m a.s.l. 4) LM was positively and significantly correlated with the annual range of temperature, basal area, non-growing season precipitation, and stem density within stands, but negatively correlated with arborous layer species richness. Furthermore, LD was positively and significantly correlated with the mean temperature of the coldest month, aspect, growing season precipitation, basal area, and stem density within stands, but negatively correlated with slope, non-growing season precipitation, and arborous layer species richness. 5) There were positively significant correlations between LM and LD in both BF and CF, whereas no significant correlation was detected in CBMF.