A study validates species–climate envelope models using 116 British breeding bird species over the past 20 years. The research evaluates the predictive accuracy of four modelling techniques—generalized linear models (GLM), generalized additive models (GAM), classification tree analysis (CTA), and artificial neural networks (ANN)—under climate change scenarios. Results show that models generally perform well on independent validation data, with ANN and GAM providing more accurate predictions than GLM and CTA. However, performance on nonindependent data tends to be overly optimistic, suggesting that models may not reliably predict future events. The study highlights the challenges of validating models due to spatial and temporal autocorrelation in data, and emphasizes the need for caution in interpreting model accuracy. It also notes that model usefulness depends on the questions being asked and the techniques used. While models can provide useful insights, they may not always be accurate, and their predictions should be contextualized appropriately. The study underscores the importance of using independent data for validation and cautions against overestimating model performance. Overall, the findings suggest that while species–climate envelope models can be valuable tools, their accuracy and reliability must be carefully assessed.A study validates species–climate envelope models using 116 British breeding bird species over the past 20 years. The research evaluates the predictive accuracy of four modelling techniques—generalized linear models (GLM), generalized additive models (GAM), classification tree analysis (CTA), and artificial neural networks (ANN)—under climate change scenarios. Results show that models generally perform well on independent validation data, with ANN and GAM providing more accurate predictions than GLM and CTA. However, performance on nonindependent data tends to be overly optimistic, suggesting that models may not reliably predict future events. The study highlights the challenges of validating models due to spatial and temporal autocorrelation in data, and emphasizes the need for caution in interpreting model accuracy. It also notes that model usefulness depends on the questions being asked and the techniques used. While models can provide useful insights, they may not always be accurate, and their predictions should be contextualized appropriately. The study underscores the importance of using independent data for validation and cautions against overestimating model performance. Overall, the findings suggest that while species–climate envelope models can be valuable tools, their accuracy and reliability must be carefully assessed.