Global climates are experiencing higher intensity, frequency, and duration of extreme events, e.g., heatwaves, droughts, and floods. Understanding community stability in the face of such extreme events is essential because stability is the key to maintaining biodiversity across time. A major past insight into community dynamics was that an aggregate property of a community, such as its total biomass, can be relatively stable through time if the species show compensatory dynamics (Gonzalez and Loreau 2009). Likewise, synchrony amplifies community biomass variability because the concordant variations of species biomass time series reinforce each other. Synchronous fluctuations can be upper-tail dependent (i.e. simultaneous higher abundance of species), which may produce years of extremely high community biomass). Alternatively, synchronous fluctuations are lower-tail dependent (i.e. simultaneous rare occurrences of species), potentially producing years of extremely low community biomass). The classic “variance ratio” (Peterson 1975) or the modified form (Loreau and de Mazancourt 2008), could not capture the effect of synchrony at the extremes on the community level and simplified it saying synchrony increases as the variance ratio increases. I have introduced a new metric - “Skewness-ratio” - which could consider a higher moment – the skewness of the community (Ghosh, Cottingham, and Reuman 2021) and can differentiate communities on stability-aspect depending on when species were synchronously common versus when species were synchronously rare. The theoretical framework of the proposed “Skewness-ratio” was also tested with two Kansas LTER datasets (Hays and Konza prairie).  This framework has a high potential to test many ecological theories across scales.