Abstract Aim General trends in spatial patterns of macroscopic organisms diversity can be reasonably well predicted from correlative models, using for instance topo-climatic variables for plants and animals allowing inference over large scales. By contrast, soil microorganisms diversity is generally considered as mostly driven by edaphic variables and, therefore, difficult to extrapolate on a large spatial scale based on predictive models. Here, we compared the power of topo-climatic vs. edaphic variables for predicting the diversity of various soil protist groups at the regional scale. Location Swiss western Alps. Taxa Full protist community and nine clades belonging to three functional groups: parasites (Apicomplexa, Oomycota, Phytomyxea), phagotrophs (Sarcomonadea, Tubulinea, Spirotrichea) and phototrophs (Chlorophyta, Trebouxiophyceae, Bacillariophyta). Methods We extracted soil environmental DNA from 178 sites along a wide range of elevations with a random-stratified sampling design. We defined protist Operational Taxonomic Units assemblages by metabarcoding of the V4 region of the ribosomal RNA small sub-unit gene. We assessed and modelled the diversity (Shannon index) patterns of all selected groups as a function of topo-climatic and edaphic variables using Generalized Additive Models. Results The respective significance of topo-climatic and edaphic variables varied among taxonomic and – to a certain extent – functional groups: while many variables explained significantly the diversity of phototrophs this was less the case for parasites. Generally, topo-climatic variables had a better predictive power than edaphic variables, yet predictive power varied among taxonomic and functional groups. Main conclusions Topo-climatic variables are, on average, better predictors of protist diversity at the landscape scale than edaphic variables, which opens the way to wide-scale sampling designs avoiding costly and time-consuming laboratory protocols. However, predictors of diversity differ considerably among taxonomic and functional groups; such relationships may be due to direct and/or indirect, e.g. biotic influences. Future prospects include using such spatial models to predict hotspots of diversity or pathogens outbreaks.