Quantile Trend Regression and Its Application to Central England Temperature

The identification and estimation of trends in hydroclimatic time series remains an important task applied climate research. statistical challenge arises from the inherent nonlinearity, complex dependence structure, heterogeneity resulting non-standard distributions underlying series. Quantile regressions are considered modeling technique for such analyses because their rich interpretation broad insensitivity to extreme distributions. This paper provides asymptotic justification quantile trend regression terms unknown structure corresponding interpretation. An empirical application sheds light on relevance analyzing monthly Central England temperature anomalies illustrates various heterogenous trends. Our results suggest presence heterogeneities across seasonal cycle increase relative frequency observing unusually high temperatures.