Precipitation variability interacts with mean precipitation to restructure a semi-arid grassland community

Climate forecasts project change not only in the mean of climate variables but also in their variance. If these dual changes interact, then future ecological dynamics will be difficult to predict using current experimental approaches, which typically change the mean or impose a single extreme event, such as drought. In a semi-arid grassland in central New Mexico, we designed a new field experiment to factorially reduce mean precipitation and increase its interannual variability. Across four years (2019-2023), drier, more variable precipitation additively reduced aboveground primary productivity of the plant community by 48-69%. Drier plus more variable precipitation interactively reduced the dominant plant species, blue grama grass, but had no effect on the plant species predicted to dominate the ecosystem in the future, which could lead to state transition. Drier, more variable precipitation also interactively reduced plant biodiversity more than either climate change factor alone, with 37-42% fewer plant species than under ambient conditions, a pattern that matched declining richness during the past 20 years of ongoing climate change. Drier, more variable precipitation restructured the composition and spatiotemporal variation of the plant community. Altered precipitation mean or variance affected 14% of plant species, with eight species sensitive to the mean × variance interaction. Results suggest that future forecasts of plant community structure may be inadequate if they fail to incorporate climate mean × variance interactions.