In this study, the Precipitation Estimates from Remotely Sensed Information using Artificial Neural Networks (PERSIANN)-Dynamic Infrared Rain-rate model (PDIR) product, using a consistently measured 40-year archive of satellite-measured cloud-top infrared temperature data with a spatiotemporal resolution of 0.04° and 3-hourly as forcing data, is used to investigate the trends in TC precipitation from 1980 to 2019 using robust linear fitting over multi-year moving averages and accumulations of TC precipitation volume and rates. Trend analysis identifies significantly increasing trends of TC precipitation across intensity classifications. The mean and upper tail precipitation rates in hurricanes are shown to be rapidly increasing, with the greatest increases found in the most extreme precipitation rates of the strongest hurricanes. Increases in TC precipitation over land across TC intensities were observed. Increased counts of precipitation-containing pixels across of all intensities per TC are robustly shown across all but the strongest TC categories. Lastly, TC properties are significantly correlated with climate oscillation values from the Atlantic Multidecadal Oscillation (AMO), North American Oscillation (NAO), and the Oceanic Nino Index (ONI) prior to the start of the hurricane season. These results suggest that an increase in TC precipitation can be observed and that the previous lack of consensus can be attributed to data with low spatiotemporal resolution, limited temporal extents, or limited coverage i.e., exclusively over land.
