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Convective heat transfer coefficients for external building surfaces (h c,ext ) are essential in building energy simulation (BES) to calculate convective heat gains and losses from building facades and roofs to the environment. These coefficients are complex functions of, among other factors, building geometry, building surroundings, building facade roughness, local air flow patterns and temperature differences. Previous research on h c,ext has led to a number of empirical models, many of which are implemented in BES programs. This paper first provides an extensive overview of such models for h c,ext calculation implemented in BES programs together with the corresponding assumptions. Next, the factors taken into account by each model are listed, in order to clarify model capabilities and deficiencies. Finally, the uncertainty related to the use of these models is discussed by means of a case study, where the use of different models shows deviations up to ± 30% in the yearly cooling energy demand (in relation to the average result) and ± 14% in the hourly peak cooling energy demand of an isolated, well-insulated building, while deviations in yearly heating energy demand are around ± 6%. The paper concludes that each model has a specific range of application, which is identified in this review paper. It also concludes that there is considerable uncertainty in the prediction of h c,ext , which can be transferred to the BES results. This large uncertainty highlights the importance of using an appropriate convection model for simulations of a specific building, certainly for calculating cooling demands and related important performance indicators such as indoor temperatures, indoor relatively humidity, thermal comfort, etc.

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• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.• You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal. Mean wind pressure coefficients (C p ) are key input parameters for air infiltration and ventilation studies. However, 9building energy simulation and stand-alone airflow network programs usually only provide and/or use a limited amount of C p 10 data, which are based on several assumptions. One of those important assumptions consists of using surface-averaged C p 11 values instead of local C p values with a high resolution in space. This paper provides information on the uncertainty in the 12 calculated airflow rate due to the use of surface-averaged C p data. The study is performed using published empirical data on 13 pressure coefficients obtained from extensive wind tunnel experiments. The uncertainty is assessed based on the comparison of 14 the airflow rate () calculated using the surface-averaged C p values ( AV ) and the airflow rate calculated using local C p values 15 ( LOC ). The results indicate that the uncertainty, with a confidence interval of 95%, is high: 0.23  AV <  LOC < 5.07  AV . In 16 cases with the largest surface-averaged C p , the underestimation or overestimation is smaller but not negligible: 0.52  AV < 17  LOC < 1.42  AV . These results provide boundaries for future improvements in C p data quality, and new developments can be 18 evaluated by comparing with the uncertainty of the current methods. 19 20