•After a clinical prediction model is developed, it is usually necessary to undertake an external validation study that examines the model's performance in new data from the same or different population. External validation studies should have an appropriate sample size, in order to estimate model performance measures precisely for calibration, discrimination and clinical utility.•Rules-of-thumb suggest at least 100 events and 100 nonevents. Such blanket guidance is imprecise, and not specific to the model or validation setting.•Our works shows that precision of performance estimates is affected by the model's linear predictor (LP) distribution, in addition to number of events and total sample size. Furthermore, sample sizes of 100 (or even 200) events and non-events can give imprecise estimates, especially for calibration.•Our new proposal uses a simulation-based sample size calculation, which accounts for the LP distribution and (mis)calibration in the validation sample, and calculates the sample size (and events) required conditional on these factors.•The approach requires the researcher to specify the desired precision for each performance measure of interest (calibration, discrimination, net benefit, etc), the model's anticipated LP distribution in the validation population, and whether or not the model is well calibrated. Guidance for how to specify these values is given, and R and Stata code is provided. Sample size “rules-of-thumb” for external validation of clinical prediction models suggest at least 100 events and 100 non-events. Such blanket guidance is imprecise, and not specific to the model or validation setting. We investigate factors affecting precision of model performance estimates upon external validation, and propose a more tailored sample size approach. Simulation of logistic regression prediction models to investigate factors associated with precision of performance estimates. Then, explanation and illustration of a simulation-based approach to calculate the minimum sample size required to precisely estimate a model's calibration, discrimination and clinical utility. Precision is affected by the model's linear predictor (LP) distribution, in addition to number of events and total sample size. Sample sizes of 100 (or even 200) events and non-events can give imprecise estimates, especially for calibration. The simulation-based calculation accounts for the LP distribution and (mis)calibration in the validation sample. Application identifies 2430 required participants (531 events) for external validation of a deep vein thrombosis diagnostic model. Where researchers can anticipate the distribution of the model's LP (eg, based on development sample, or a pilot study), a simulation-based approach for calculating sample size for external validation offers more flexibility and reliability than rules-of-thumb.