Seasonal trend assessment of US extreme precipitation via changepoint segmentation

Abstract

Most climate trend studies analyze long-term trends as a proxy for climate dynamics. However, when examining seasonal data, it is unrealistic to assume that long-term trends remain consistent across all seasons. Instead, each season likely experiences distinct trends. Additionally, seasonal climate time series, such as seasonal maximum precipitation, often exhibit nonstationarities, including periodicities and location shifts. Failure to rigorously account for these features in modeling may lead to inaccurate trend estimates. This study quantifies seasonal trends in the contiguous United States' seasonal maximum precipitation series while addressing these nonstationarities. To ensure accurate trend estimation, we identify changepoints where the seasonal maximum precipitation shifts due to factors like measurement device changes, observer differences, or location moves. We employ a penalized likelihood method to estimate multiple changepoints, incorporating a generalized extreme value distribution with periodic features. A genetic algorithm based search algorithm efficiently explores the vast space of potential changepoints in both number and timing. Additionally, we compute seasonal return levels for extreme precipitation. Our methods are illustrated using two selected stations, and the results for the US are summarized through maps. We find that seasonal trends vary more when changepoints are considered than in studies that ignore them. Our findings also reveal distinct regional and seasonal patterns, with increasing trends more prevalent during fall in the South and along the East Coast when changepoints are accounted for.