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ABSTRACT Predicting the distribution, structure, and biomass of mangrove forests is an area of high research interest. Across the Atlantic East Pacific biogeographic region, three species are common and abundant members of local mangrove communities; Rhizophora mangle, Avicennia germinans, and Laguncularia racemosa. Biomass prediction for these species has relied on two approaches: site‐specific allometries based on the idea that environmental/climatic differences between sites drive growth differences, or the use of common allometric equations based on the idea that site driven differences are minimal. Meta‐analyses of global compilations of interspecific plot level data (e.g., mean canopy height, stand basal area) show trends in size and structure with climatic variables, however this has not been critically evaluated across these species using empirical allometric growth functions. We compared allometric equations derived from 590 individuals within and across nine broadly distributed sites at interspecific and intraspecific levels and explored the influence of climatic variables on allometric slopes and intercepts. Assessing variables that can be used to predict biomass in the field (height, diameter at breast height (DBH), canopy spread), we find interspecific root mean squared errors similar to or smaller than most intraspecific or site‐specific equations, particularly when examining sites with sample sizes above recommended values. We also find significant effects of several climatic variables on growth allometries with the strongest effects from minimum temperature followed by precipitation seasonality. Our results suggest that while climate has a clear influence on mangrove allometric growth, common equations may have utility in biomass prediction. Future methodological improvements, particularly larger sample sizes across the entire available size range, combined with data from a broader range of growth conditions will further inform which allometric relationships exhibit the most variability within and across sites and which variables best predict mangrove biomass.