Forest management aimed at promoting climate change resilience hinges on accurately quantifying the relationship between tree growth and climate. Aggregation is commonly used to upscale individual tree response (e.g., ring-width time series) to broader scales of inference, prediction, and decision-making. This approach assumes non-climatic drivers of tree growth vary randomly across a population such that their effects cancel out with replication and climate emerges as a strong predictor of aggregate tree growth.