Is unequal weighting significantly better than equal weighting for multi‐model forecasting?

Abstract This article proposes a statistical test for whether a multi‐model combination with unequal weights has significantly smaller errors than a combination with equal weights. A combination with equal weights includes the case of a no‐skill model, in which all weights equal zero, and the multi‐model mean, in which all weights equal 1 /M , where M is the number of models. The test is applied to seasonal hindcasts of 2 m temperature and precipitation generated by five state‐of‐the‐art coupled atmosphere–ocean models. The hypothesis of equal weights could not be rejected over 75% the globe for temperature and 90% of the land for precipitation, implying that strategies for unequal weighting of forecasts may be of value only over a relatively small fraction of the globe. The fact that the test does not require pre‐specifying a specific strategy for weighting forecasts suggests that it should be useful for exploring a wide range of multi‐model strategies. Copyright © 2012 Royal Meteorological Society