Spatiotemporal analysis of compound hot-dry and hot-wet extreme events over Tanzania

Abstract Although there is growing literature on single‑variable extremes (drought, heavy rainfall, heatwaves), there are limited studies that explicitly examine compound hot–wet and hot–dry events across Tanzania. This study investigated the spatiotemporal characteristics and drivers of compound hot–dry and hot–wet extreme events across Tanzania during 1981–2023. Using the Climatic Research Unit dataset, compound extremes were identified based on concurrent anomalies in monthly temperature and precipitation exceeding the 75th percentile and falling below the 25th percentile thresholds, respectively. The results show pronounced spatial and seasonal variability. Hot-wet extremes are predominantly observed in central and southern regions, with a peak during the March-May (MAM) long rains, while hot–dry extremes are more frequent across the northern and western areas, intensifying during the October-December (OND) short rains. Both hot-wet and hot-dry events have intensified over recent decades, suggesting growing climatic risks. Trend analyses indicate significant increases in hot-wet frequencies during OND and June to September (JJAS), and in both hot-wet and hot-dry during the extended wet and climatologically dry JJAS seasons. Analysis of large-scale oceanic drivers reveals a dominant and nonlinear synergistic influence from the El Niño–Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD). Composite and correlation analyses show that hot-wet extremes are strongly associated with El Niño and positive IOD phases, while hot-dry extremes coincide with La Niña and negative IOD conditions. The concurrent positive ENSO–IOD phases produce the most widespread hot-wet anomalies, explaining up to 62% of the interannual variance in hot-wet events during the January-February season. These findings highlight the increasing frequency of compound climate extremes in Tanzania and reveal substantial predictability linked to large-scale oceanic variability. The significant multi-month lagged correlations demonstrate promising potential for integrating compound-event diagnostics into subseasonal-to-seasonal forecasting and early warning systems.