The recent advancements of new quantitative tools compatible with plant macrofossil proxy data have revived its potential for paleoclimate research. Plant macrofossils are commonly used in so-called indicator-species approaches, using methodologies that are typically built on known observations linking modern plant distributions with climate. This allows complementary paleoclimate reconstructions using an approach that is not limited by the spatial availability of calibration samples obtained from surface sediments (e.g., pollen or chironomids). We aim to evaluate the impact that various methodological choices have on the plant-macrofossil based reconstructions of January and July temperature patterns for the Lateglacial (14–11 ka BP) period. We use a variety of classic and novel quantitative climate reconstruction algorithms with plant macrofossil assemblages from 13 sites of the Baltic States. We use unfiltered plant data to evaluate the ability of each method to also handle the presence of plants that might have a weak sensitivity to temperature. Additionally, we test the influence of another methodological choice – the choice of modern calibration region – on the reconstructed climate. Our findings indicate that, with no prior filtering of summer and winter-sensitive plants, temporal temperature variations can be reconstructed with methods that implement probability density functions. Although some disparities in reconstructions are seen between the tested algorithms, we note that the choice of calibration region bears a greater influence on the results. A calibration region that best represents the past environment should be chosen rather than one representing the same spatial extent as the fossil site(s). Moreover, for long-term reconstructions, a “dynamic calibration set” approach should be considered in future studies by using a range of calibration regions and mirroring the continuously changing broadscale environmental regime of the past. •Seasonal temperature reconstructions vary across climate reconstruction algorithms.•Calibration region impact is greater in January temperature reconstructions than July.•Proper calibration region evaluation is crucial before reconstructions.•Calibration regions should be based on environmental instead of geographical fit.