Plant DNA methylation changes occur hundreds up to thousands times faster than DNA mutations and can be transmitted transgenerationally, making them useful for studying population-scale patterns in clonal or selfing species. However, a state-of-the-art approach to use them for inferring population genetic processes and demographic histories is lacking. To address this, we compare evolutionary signatures extracted from CG methylomes and genomes in Arabidopsis thaliana and Brachypodium distachyon. While methylation variants (SMPs) are less effective than genetic variants (SNPs) for identifying population differentiation inA. thaliana, they can classify phenotypically divergentB. distachyonsubgroups that are otherwise genetically identical. The site frequency spectra generated using methylation sites from varied genomic locations and evolutionary conservation exhibit similar shapes indicating minimal noise when jointly analyzing all CG sites. Nucleotide diversity is three orders of magnitude higher for methylation variants compared to genetic variants in both species, driven by the higher epimutation rate. Correlations between SNPs and SMPs in nucleotide diversity and allele frequencies at gene exons are weak or absent inA. thaliana, possibly because the two sources of variation reflect evolutionary forces acting at different timescales. Linkage disequilibrium quickly decays within 250bp for methylation variants in both plant species suggesting their versatility for evolutionary analyses. Finally, we developed a deep learning-based demographic inference approach and identified recent population expansions inA. thalianaandB. distachyonusing methylation variants that were not identified when using genetic variants. Our study demonstrates the unique evolutionary insights provided by methylomes that genetic variation alone cannot reveal.