Climate warming has triggered shifts in plant distributions, resulting in changes within communities, characterized by an increase in warm-demanding species and a decrease in cold-adapted species - referred to as thermophilization. Researchers conventionally rely on co-occurrence data from vegetation assemblages to examine these community dynamics. Despite the increasing availability of presence-only data in recent decades, their potential has largely remained unexplored due to concerns about their reliability. Our study aimed to determine whether climate-induced changes in community dynamics, as inferred from presence-only data from the Global Biodiversity Information Facility (GBIF), corresponded with those derived from co-occurrence plot data in Norway. To assess the differences between these datasets, we quantified a Community Temperature Index (CTI) from the co-occurrence data set and compared this with CTI obtained from presence-only data. We also examined the temporal trend in CTI (i.e., thermophilization) in both datasets. To do this, we first established a species-temperature relationship based on data before climate warming. In a preliminary analysis, we assessed the performance of this relationship using three datasets: 1) Norwegian co-occurrence data, 2) presence-only data from a broader European region organized into pseudo-plots (potentially capturing more species niches), and 3) a combined dataset merging 1) and 2). The transfer function including both datasets performed best. Subsequently, we compared the CTI for the co-occurrence plots paired up spatially and temporally with presence-only pseudo-plots. The results demonstrated that presence-only data can effectively evaluate species assemblage responses to climate warming, with consistent CTI and thermophilization values in comparison to co-occurrence data. Employing presence-only data for evaluating community responses opens up better spatial and temporal resolution and much more detailed analyses of such responses, our results therefore outline how a large amount of presence-only data can be used to enhance our understanding of community dynamics in a warmer world.