Rare Event Simulation of Extreme European Winter Rainfall in an Intermediate Complexity Climate Model

We test the application of a rare event simulation (RES) algorithm to accelerate the sampling of extreme winter rainfall over Europe in a climate model. The genealogical particle analysis algorithm, an ensemble method that interrupts the simulation at intermediate times to clone realizations in which an extreme event is developing, is applied to the intermediate complexity general circulation model PlaSim. We show that the algorithm strongly reduces the numerical effort required to estimate probabilities of extremes, demonstrating the potential of RES of seasonal precipitation extremes.Plain Language Summary Rare events, such as winters with a lot of rainfall, can have a high impact. High rainfall can cause floods, risking lives, and damaging property. To plan for such events, we want to know what the probability is to experience a lot of rainfall in any given year. One way to make an estimate is to run climate models and look at the proportion of winters having rainfall above a high threshold. This method can be very costly and time-consuming, as climate models require powerful computers and we need to run them for a long time until we see a few winters with very high rainfall. In this article, we apply algorithms to speed up such model simulations. Instead of keeping the model running for a long time, we run a large number of copies at the same time. At regular intervals, we check which simulations are producing a lot of rain. These we copy, while others are terminated. Even though we have made high rainfall more likely than it should be, we can still estimate what the correct probabilities of high rainfall are. We show that this method saves time running our computer model, while still giving accurate results.