Precipitation Scaling With Temperature in the Northeast US: Variations by Weather Regime, Season, and Precipitation Intensity

We present a novel hierarchical Bayesian quantile regression model to estimate how precipitation scales with temperature depending on season, weather regime (WR; i.e., large-scale patterns of atmospheric circulation), and precipitation percentile. The approach develops regional scaling estimates by partially pooling data across sites, accounting for uncertainty stemming from variable record lengths. Results using long-term records of daily and hourly precipitation and both dry-bulb and dew point temperature across the Northeast US suggest that regional precipitation scaling rates vary from 0% to 8% per degrees C. Scaling rate variability is driven most by season and the percentile of precipitation, with only modest variations across WRs. Daily scaling rates are highest in the winter and summer, while hourly rates at the highest percentiles are greatest in summer. Lower scaling rates for more extreme precipitation quantiles are driven by a handful of storms occurring at cooler temperatures but with strong vertical uplift and heavy precipitation.

Automated summary

A novel hierarchical Bayesian quantile regression model is proposed to estimate the relationship between precipitation and temperature in different seasons, weather regimes, and precipitation percentiles. The study finds that the scaling rates of regional precipitation vary depending on the season and percentile, while the variations across different weather regimes are modest.