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Ensembles of air quality models have been formally and empirically shown tooutperform single models in many cases. Evidence suggests that ensemble erroris reduced when the members form a diverse and accurate ensemble. Diversityand accuracy are hence two factors that should be taken care of whiledesigning ensembles in order for them to provide better predictions.Theoretical aspects like the bias–variance–covariance decomposition and theaccuracy–diversity decomposition are linked together and support theimportance of creating ensemble that incorporates both these elements. Hence,the common practice of unconditional averaging of models without priormanipulation limits the advantages of ensemble averaging. We demonstrate theimportance of ensemble accuracy and diversity through an inter-comparison ofensemble products for which a sound mathematical framework exists, andprovide specific recommendations for model selection and weighting<i>for multi-model ensembles</i>. The sophisticated ensemble averagingtechniques, following proper training, were shown to have higher skill acrossall distribution bins compared to solely ensemble averaging forecasts

&lt;i&gt;De praeceptis ferendis&lt;/i&gt;: good practice in multi-model ensembles

<i>De praeceptis ferendis</i>: good practice in multi-model ensembles