Both monetary and macroprudential policymakers use conditional density forecasts of GDP to inform their policy decisions. The former tend to use ‘fan charts’ that convey uncertainty around central projections from linearised macroeconomic models. The latter draw on estimates of ‘growth-at-risk’, typically estimated using quantile-regression techniques, reflecting their focus on the tails of the GDP distribution. Focusing on the UK, we study how the fan charts constructed by the Bank of England’s Monetary Policy Committee compare to predictive densities derived from statistical growth-at-risk models. We find that GDP-at-risk models provide improved estimates of the left-tail of the GDP-growth distribution, in particular at medium-term horizons compared to the GDP fan charts. However, GDP-at-risk models generally perform worse than the fan charts at the centre of the distribution. Combining forecast densities in a parsimonious manner provides the best forecasts, with limited losses compared to optimal density combination methods. Doing so offers central banks the opportunity to improve GDP density forecasts, and unify the narrative provided by monetary and macroprudential policymakers about the economic outlook and uncertainty around it.