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Considering the statistical performance of global probabilistic forecasts of 850 hPa and 2 m temperatures, a multimodel ensemble containing nine ensemble prediction systems (EPS) from the TIGGE archive did not improve on the performance of the best single\u2010model, the ECMWF EPS. However, a reduced multimodel system, consisting of only the four best ensemble systems, provided by Canada, the USA, the United Kingdom and ECMWF, showed an improved performance. The multimodel ensemble provides a benchmark for the single\u2010model systems contributing to the multimodel. However, reforecast\u2010calibrated ECMWF EPS forecasts were of comparable or superior quality to the multimodel predictions, when verified against two different reanalyses or observations. This improved performance was achieved by using the ECMWF reforecast dataset to correct for systematic errors and spread deficiencies. The ECMWF EPS was the main contributor for the improved performance of the multimodel ensemble; that is, if the multimodel system did not include the ECMWF contribution, it was not able to improve on the performance of the ECMWF EPS alone. These results were shown to be only marginally sensitive to the choice of verification dataset. 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