Biosecurity agencies are particularly concerned to know the potential distribution of invasive alien species under present, and to a lesser extent, future climates; expensive decisions can hinge upon the degree of perceived threat a pest species poses. Climate-based niche modelling techniques are available to inform these decisions. These tools now regularly employ gridded climate datasets of moderate spatial resolution (0.5 degree), though biosecurity decision-makers continually seek greater spatial precision in the risk map products. Various splining techniques are capable of generating gridded climate datasets approaching the precision limits imposed by the availability of digital elevation model data. As the spatial precision of climate datasets increases, more detailed effects of topographic relief become apparent in the climatic data. When these datasets are used to develop and apply species niche models, the climate data is spatially intersected with species location data to infer relationships between the climate and the species’ geographic distribution. Here we investigate the effect of changing climate precision on projections of species’ niche models developed with CLIMEX, including the effect of upscaling and downscaling the outputs. We found that there were noticeable increases in sensitivity in models developed using more precise climate datasets. The largest differences in projections were noted where species range limits coincided with regions of strong climatic gradients such as where there was marked topographic relief in relation to the spatial precision of the climatic dataset. Upscaling (fitting a model with a fine resolution dataset and then projecting the results with a coarser grid), tended to produce smaller potential ranges for a species, albeit at the cost of model sensitivity. Downscaling had the opposite effect, identifying additional, mostly marginally climatically suitable habitat. It remains unclear how sensitive the fine resolution results are to the number and spatial arrangement of input location records used to build the model. The results indicate some benefits of improving the spatial resolution of climate datasets, though not at the expense of climatic data accuracy. Decision-makers should be mindful of the inherent uncertainties in these models, and modellers have a responsibility to identify and convey these uncertainties to their intended audience.