Aim Species distribution models (SDMs) are currently the most widely used tools in ecology for evaluating the suitability of environments for biodiversity in the face of future environmental change. In this study we seek to provide an assessment of the predictive performance of SDMs over time. How well do SDMs predict for future time periods and what factors influence predictive performance? Innovation We used a historical spatially explicit database of 1.8 million occurrence records for 318 tetrapod species from across continental Australia over the period 1950–2013. We fitted distribution models for each species to data from four multi‐decadal time slices and used these to predict the species distributions up to 60 years after the data collection period for the fitted models. We evaluated predictions against observed data from the relevant time period. Predictions were made assuming either complete knowledge of changes in climatic and environmental conditions or assuming the environment and climate remained unchanged between the fitting and evaluation time periods. We used generalized linear mixed models to model variation in the predictive performance of SDMs over time in relation to a variety of factors, including the length of time between fitting and evaluation, species traits, taxonomic group and attributes of the dataset used to fit models. Main conclusions We found that most models provided useful predictions even when the period between model fitting and evaluation was 60 years (area under the receiver operator characteristic curve > 0.7 in 80% of the species evaluated). Variation in predictive performance over time was strongly related to the species range breadth (models for species with broad geographical ranges tended to perform worse than models for locally restricted species) and to the environmental coverage of occupancy data. Conversely, taxonomic group, habitat preferences and body size were not highly influential in describing the variation in predictive performance over time.

Conservation of species under climate change relies on accurate predictions of species ranges under current and future climate conditions. To date, modelling studies have focused primarily on how changes in long‐term averaged climate conditions are likely to influence species distributions with much less attention paid to the potential effect of extreme events such as droughts and heatwaves which are expected to increase in frequency over coming decades. In this study we explore the benefits of tailoring predictor variables to the specific physiological constraints of species, or groups of species. We show how utilizing spatial predictors of extreme temperature and water availability (heat‐waves and droughts), derived from high‐temporal resolution, long‐term weather records, provides categorically different predictions about the future (2070) distribution of suitable environments for 188 mammal species across different biomes (from arid zones to tropical environments) covering the whole of continental Australia. Models based on long‐term averages‐only and extreme conditions‐only showed similarly high predictive performance tested by hold‐out cross‐validation on current data, and yet some predicted dramatically different future geographic ranges for the same species under 2070 climate scenarios. Our results highlight the importance of accounting for extreme conditions/events by identifying areas in the landscape where species may cope with average conditions, but cannot persist under extreme conditions known or predicted to occur there. Our approach provides an important step toward identifying the location of climate change refuges and danger zones that goes beyond the current standard of extrapolating long‐term climate averages.

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