We show that a Markov-switching model with endogenous transition probabilities can replicate a common finding in the Growth-at-Risk literature, that is that the (conditional) mean and volatility of future growth are negatively correlated. The model also provides an intuitive interpretation of macroeconomic risk: (endogenous) regime uncertainty generates tail risk. The higher the regime uncertainty, the starker are the differences in the growth outlook between a normal and a bad state of the economy. The model is a new tool to assess the risk of tail events, such as recessions, and to evaluate the likelihood of point forecasts. We also propose real-time measures of financial conditions and economic activity for the United States and use these measures to construct conditional quantiles and predictive distributions of average GDP growth over the next 12 months. We find that periods of high macroeconomic and financial distress, such as the Global Financial Crisis and the COVID-19 pandemic, are associated with low average future growth, high uncertainty, and risks tilted to the downside.