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Background: Pillar 4 of the United States' End the HIV Epidemic plan is to respond quickly to HIV outbreaks, but the utility of CDC's tool for identifying HIV outbreaks through time–space cluster detection has not been evaluated. The objective of this evaluation is to quantify the ability of the CDC time–space cluster criterion to predict future HIV diagnoses and to compare it to a space–time permutation statistic implemented in SaTScan software. Setting: Washington State from 2017 to 2019. Methods: We applied both cluster criteria to incident HIV cases in Washington State to identify clusters. Using a repeated-measures Poisson model, we calculated a rate ratio comparing the 6 months after cluster detection with a baseline rate from 24 to 12 months before the cluster was detected. We also compared the demographics of cases within clusters with all other incident cases. Results: The CDC criteria identified 17 clusters containing 192 cases in the 6 months after cluster detection, corresponding to a rate ratio of 1.25 (95% confidence interval: 0.95 to 1.65) relative to baseline. The time–space permutation statistic identified 5 clusters containing 25 cases with a rate ratio of 2.27 (95% confidence interval: 1.28 to 4.03). Individuals in clusters identified by the new criteria were more likely to be of Hispanic origin (61% vs 20%) and in rural areas (51% vs 12%). Conclusions: The space–time permutation cluster analysis is a promising tool for identification of clusters with the largest growth potential for whom interruption may prove most beneficial. [ABSTRACT FROM AUTHOR]