This is a companion dataset to the paper "Precipitation Variability in CMIP6 Climate Models across the North Atlantic–European region and their Links to Atmospheric Circulation" by E. Plavcová submitted to Climate Dynamics.

Long-term changes in climate variability represent an important aspect of climate change, with various impacts on society and environment. In this study, we analyze outputs from 13 CMIP6 global climate models (GCMs) across the North Atlantic–European domain, focusing on their simulations of precipitation probability and short-term variability in both historical and future climates. Precipitation probability denotes the probability of a wet day (> 1 mm), and precipitation variability reflects the tendency to cluster wet days into sequences. By comparing against the ERA5 reanalysis, we found that the GCMs tend to overestimate precipitation probability across Europe in winter, whereas in summer, they have a tendency to underestimate it around 50°N. Precipitation variability is, on average, underestimated by the GCMs in summer, while overestimated in several regions in winter. Projections for the end of the 21st century indicate significant changes in both precipitation probability and variability which are more pronounced under the more pessimistic emission scenario compared to the moderate one. We found that the changes in probability and variability are mutually independent: the former being more latitudinal-dependent while the latter differs between the west and east. After identifying atmospheric circulation conducive and non-conducive to precipitation occurrence, we found that GCMs overestimating the frequency of conducive circulation tend to overestimate precipitation probability, and vice versa. Furthermore, increased precipitation variability is associated with higher circulation variability. Finally, our analysis reveals that projected changes in precipitation probability and variability are often linked to projected changes in atmospheric circulation, especially in winter.