Atmospheric Moisture Intensification and the Recurrence Structure of Heavy Rainfall Events in a Warming Climate
DOI:
https://doi.org/10.71465/jgs60Keywords:
Atmospheric moisture, rainfall recurrence, Poisson process, Markov model, climate changeAbstract
This study examines how atmospheric moisture intensification reshapes the recurrence structure of heavy rainfall events using ERA5 reanalysis (hourly, 0.25°, 1980–2023, ~6.4 million samples) and CMIP6 projections. A hybrid stochastic framework combining a first-order Markov chain for event transition probabilities and a non-homogeneous Poisson process for inter-arrival times is developed. Model parameters are conditioned on precipitable water (PW) and temperature anomalies. Results show that PW increases at a rate of 7.1–7.4% per °C warming, leading to a reduction in mean recurrence intervals of extreme rainfall from 5.8 days to 4.1–4.6 days (−21% to −29%). The probability of consecutive extreme events increases from 0.18 to 0.27 under high-emission scenarios. Spectral density analysis indicates a shift toward higher-frequency variability bands (<10 days). The model explains 86% of observed variance in recurrence statistics and captures clustering behavior with a Kullback–Leibler divergence below 0.05 compared to observations. These findings reveal a systematic transition toward more temporally aggregated and frequent rainfall extremes under warming conditions.
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Copyright (c) 2026 Rachel Chan, Jonathan Lee (Author)
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All articles published in the Journal of Geospace Science (JOGSS) are licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This license allows others to share, copy, distribute, and adapt the work, provided proper attribution is given to the original author(s) and source.