High-frequency data can provide us with a quantity of informa- tion for forecasting, help to calculate and prevent the future risk based on extremes. This tail behaviour is very often driven by ex- ogenous components and may be modelled conditional on other vari- ables. However, many of these phenomena are observed over time, exhibiting non-trivial dynamics and dependencies. We propose a func- tional dynamic factor model to study the dynamics of expectile curves. The complexity of the model and the number of dependent variables are reduced by lasso penalization. The functional factors serve as a low-dimensional representation of the conditional tail event, while the time-variation is captured by factor loadings. We illustrate the model with an application to climatology, where daily data over years on temperature, rainfalls or strength of wind are available.