How much do serial correlation and field significance affect trend detection on extreme precipitation frequencies?

According to both theoretical considerations and climatic projections, precipitation extremes are expected to increase under a warmer environment. Inferential analyses involving statistical testing procedures are frequently performed to validate this scenario. Recent research has found that the results of trend tests applied to hydrological data might be misinterpreted if (i) records exhibit autocorrelation and (ii) field significance is not taken into account when tests are performed multiple times. In this study, we investigate these two issues focusing on frequencies (or counts) of daily rainfall extremes. To this end, a sample of extreme precipitation frequency time series is derived from long-term (100-year) daily precipitation records retrieved by 1087 rain gauges belonging to the Global Historical Climate Network database. Several Monte Carlo simulations are performed involving random synthetic frequency time series generated through the Poisson first-order Integer-valued AutoRegressive model (Poisson-INAR(1)), reproducing the statistical properties of the observed counts and characterized by different sample size, autocorrelation level, and trend magnitude. The following are the key findings. (1) While empirical autocorrelations are likely due to the existence of trends, empirical trends cannot be explained solely by autocorrelation, suggesting that accounting for serial correlation may have a limited influence on trend analyses of extreme frequency time series. (2) Taking field significance into account enhances the interpretation of test results by reducing the type-I errors. (3) Parametric statistical trend tests based on linear and Poisson regression prove more powerful than non-parametric tests (e.g. Mann-Kendall test) in analyzing count series. (4) Finally, we use these insights to conduct trend assessments on observed counts, finding several clear spatial patterns of statistically significant increasing (decreasing) trends, mostly located in central and eastern United States and Northern Eurasia (southwestern United States, southern Europe, southern parts of Australia).