Bioclimatic zoning is a powerful tool for generalizing construction guidelines for buildings aiming to improve their energy performance and thermal comfort, among other features. Reliable and accurate climate data is crucial for developing an effective zoning method. Additionally, other uncertainties, such as microclimates and future climate conditions, can also highly influence the resulting zoning. The present research aims to investigate how uncertainty regarding climate data can influence climate classification, considering the current bioclimatic zoning method for Brazil and exploring the impact of climate database accuracy, microclimates, and future climate behavior. To do this, the accuracy of climate data is assessed by comparing the standard weather data (i.e., recent typical meteorological years) with equivalent data from reanalysis (ERA5-Land) and an artificial neural network model. The Urban Weather Generator software is employed to model the influence of microclimates. This approach is calibrated based on dry-bulb temperature from an urbanized meteorological station using particle swarm optimization and subsequently applied in local climate zones. The climate change analysis is performed considering two emission scenarios (RCP2.6 and RCP8.5), three GCM models (HadGEM2, MPI-ESM, and NorESM1), and two RCM models (RegCM and REMO). All Brazilian cities (5570) are analyzed, but a deeper investigation is conducted in 34 representative cities. Errors in high spatial resolution data are less than 0.4 °C for temperature and 1.6% for relative humidity throughout Brazil. The urban microclimate causes a difference of 0.61 °C in annual mean temperature among urban contexts. Regarding climate change, the annual mean temperature tends to increase over time in Brasília, regardless of the emission scenario, ranging from 1.5 °C to 5.4 °C by 2090. The results show that the three sources of uncertainty analyzed can significantly impact the bioclimatic classification of the studied Brazilian cities.