Abstract Introduction Understanding the differences in carbon and water vapor fluxes of spatially distributed evergreen needleleaf forests (ENFs) is crucial for accurately estimating regional or global carbon and water budgets and when predicting the responses of ENFs to current and future climate. Methods We compared the fluxes of ten AmeriFlux ENF sites to investigate cross-site variability in net ecosystem exchange of carbon (NEE), gross primary production (GPP), and evapotranspiration (ET). We used wavelet cross-correlation analysis to examine responses of NEE and ET to common climatic drivers over multiple timescales and also determined optimum values of air temperature (T a) and vapor pressure deficit (VPD) for NEE and ET. Results We found larger differences in the NEE spectra than in the ET spectra across sites, demonstrating that spatial (site-to-site) variability was larger for NEE than for ET. The NEE and ET were decoupled differently across ENF sites because the wavelet cospectra between ET and climate variables were similar at all sites, while the wavelet cospectra between NEE and climate variables were higher (i.e., closer coupling between NEE and climatic drivers) in semi-arid and Mediterranean sites than in other sites. Ecosystem water use efficiency (EWUE) based on annual GPP/ET ranged from 1.3 ± 0.18 to 4.08 ± 0.62 g C mm−1 ET, while EWUE based on annual net ecosystem production (NEP)/ET ranged from 0.06 ± 0.04 to 1.02 ± 0.16 g C mm−1 ET) among ENFs. Responses of NEE and ET to T a varied across climatic zones. In particular, for ENF sites in semi-arid and Mediterranean climates, the maximum NEE and ET occurred at lower ranges of T a than in sites with warm and humid summers. The optimum T a and VPD values were higher for ET than for NEE, and ET was less sensitive to high values of T a and VPD. Conclusions Large spatial variability in carbon and water vapor fluxes among ENFs and large variations in responses of NEE and ET to major climate variables among climatic zones necessitate sub-plant functional type parameterization based on climatic zones to better represent climate sensitivity of ENFs and to reduce uncertainty in model predictions.