Surrogate-assisted evolutionary algorithms approximate expensive objective functions, but they introduce prediction uncertainty that can misdirect the search. We propose uncertainty quantification using conformal prediction with temporal weighting for non-stationary population dynamics. As populations concentrate around promising regions, older training data becomes less representative. Our temporal weighting adaptively prioritises recent observations when calibrating prediction intervals. We evaluate on BBOB and CEC2013 benchmarks (2–50 dimensions) with varying budgets. Temporal weighting achieves empirical coverage of 0.71–0.89 (CMA-ES) and 0.63–0.84 (GA), improving 20–45% points over non-adaptive methods. Coverage varies by structure: unimodal functions achieve 0.85–0.91, compositional functions 0.54–0.76. Compared to purely surrogate-based selection, our approach reduces evaluations by 8–15% while maintaining comparable fitness. The framework provides practical uncertainty estimates for efficient surrogate-assisted optimisation under computational constraints.