Statistical modelling of spatio-temporal rainfall trends, dependence, and extremes in Zimbabwe (1984–2024)

Abstract

Understanding the spatio-temporal variability of extreme rainfall is critical for climate adaptation and for informed water-resource planning in Zimbabwe. This study develops a unified statistical framework to: (1) characterize annual rainfall variability and detect monotonic trends; (2) model extreme rainfall events via block maxima and peaks-over-threshold methods and quantify their long-range dependence; (3) assess the spatial autocorrelation patterns of rainfall across provinces; and (4) explore inter-provincial rainfall dynamics using a Vector Autoregressive (VAR) model. Monthly rainfall totals for ten provinces (1984–2024) were retrieved from NASA POWER at provincial centroids. Descriptive statistics reveal a pronounced east–west gradient in mean annual rainfall (390.1 mm in Manicaland; 190.8 mm in Matabeleland South) and variability (Standard deviation up to 147 mm in Manicaland). The Mann–Kendall and block-bootstrap tests indicate no significant monotonic trends (all 0.16), suggesting stationary annual totals over the study period. Extreme-value analysis shows uniformly positive Generalized Extreme Value (GEV) shape parameters (for example) with anomalously large estimates in Harare and the Midlands; Generalized Pareto Distribution (GPD) fits yield moderate heavy-tails () and scale parameters of 5.6–9.9 mm. Detrended fluctuation analysis produces Hurst exponents (0.25–0.35), indicating anti-persistence and mean-reversion in extreme-rainfall maxima. Spatial autocorrelation metrics (Global Moran’s I = 0.266; Geary’s) confirm significant clustering. Local Moran’s I identifies northeastern hot-spots and southwestern cold-spots. A VAR(2) model—selected via minimum Akaike Information Criterion/Bayesian Information Criterion (also known as the Schwarz Criterion)(AIC/BIC)—highlights significant two-month persistence in Manicaland (,) and one-month memory in Matabeleland North (,). These findings reveal heterogeneous heavy-tailed behaviour, mean-reversion, and spatial clustering in Zimbabwe’s rainfall extremes, underscoring the need for region-specific risk assessments and infrastructure design tailored to local hydro-climatic regimes.