Abstract: Extensive ecological research has investigated the impact of extreme climate events or long-term changes in average climate variables, but changes in year-to-year (interannual) variability may also cause important biological responses, even if the climate mean is stable. The environmental stochasticity that is a hallmark of climate variability can trigger unexpected biological responses that include tipping points to state transitions. Large differences in weather between consecutive years can also propagate antecedent effects, in which current biological responses depend on responsiveness to past perturbations. Because the ecological consequences of increases in climate variance can depend on the climate mean in complex ways, effective ecological predictions will require determining responses to both non-stationary components of climate distributions: the mean and the variance. However, most studies to date cannot predict ecological responses to rising variance because the study of interannual variance requires empirical platforms that generate long time series. We crafted a new experimental design to resolve the relative importance of, and interactions between, a drier mean and greater variance of precipitation, which are dual components of ongoing climate change in the southwestern USA. The Mean × Variance Experiment (MVE) adds two novel elements to prior field infrastructure with (i) factorial manipulation of variance together with the climate mean and (ii) the creation of realistic and stochastic precipitation regimes. Here, we demonstrate the efficacy of this design. We designed and built sensor networks and PhenoCams for automated monitoring, and we replicated MVE across ecosystem types at the northern edge of the Chihuahuan Desert biome as a central component of the Sevilleta Long-Term Ecological Research Program. At our longest-running site, soil sensors detected significant treatment effects on both the mean and interannual variability in soil moisture, and PhenoCam imagery captured treatment effects on vegetation cover. Twice yearly surveys of vegetation quadrats detected interactive effects of a drier mean and increased variance, which reduced aboveground primary production relative to current climate conditions. Cohorts of seed additions of the current foundation plant species and the species predicted to dominate in the future revealed that ambient climate favors the current foundation species, while a drier and more variable climate of the future favors recruitment of the species most likely to cause a state transition. Our design represents an advance in field methods that enables new comparisons of the sensitivities of populations, communities, and ecosystem processes to climate mean × variance interactions.