Gridded TerraClimate and ERA5-Land reanalysis datasets provide high-resolution climate data of either direct vapour pressure deficit (VPD) or its input variables at 0.04 and 0.1° spatial resolution respectively, and over extended time periods. However, there are gaps in their applicability over data-sparse regions such as East Africa. This study aims to (i) validate VPD from both gridded TerraClimate VPD and ERA5-Land reanalysis using station-derived VPD for the period 2014–2023 and (ii) compare and contrast the two datasets at a monthly timescale over the period 1984–2023 in a spatiotemporal context. We employed the root mean square error (RMSE), scatter index (SI) and correlation coefficients for accuracy evaluation against 20 meteorological stations across East Africa. For spatiotemporal comparison between the two datasets, we used the Mann–Kendall trend test, Student’s t-test and correlation analysis. Results showed that ERA5-Land reanalysis generally performed better across most parts of East Africa, as evidenced by strong correlations and low SI scores ranging within 0–30% for 95% of the stations, compared to a good-fair performance of gridded TerraClimate with SI scores of 0–50% for 95% of the stations. Finally, comparative analysis revealed strong agreement between the two datasets on trend magnitude, direction and significance as well as June-to-August seasonal means. Additionally, very strong positive correlations (0.75–1.0) were observed for most parts of East Africa, except over areas dominated by very high humidity such as mountainous areas and those surrounding lakes. Finer temporal- and spatial-scale studies are recommended, with emphasis on exploring the added value of combining the two datasets.

Uganda’s economy hinges on sectors with the highest sensitivity to rainfall variability. The lack of a comprehensive understanding of the nature and dynamics of rainfall seasons has thus profoundly impacted it. We modify and apply a dual-phase seasonal search scheme to characterize rainfall seasonal performance over Uganda from 1990 to 2024, using a state-of-the-art Climate Hazards Group Infrared Precipitation with Stations (CHIRPS), version 3 (CHIRPS3). We apply novel validation, anomaly, trend, mean difference, hotspot, and regression analyses in a spatiotemporal context. Results reveal superior performance of CHIRPS3 against ground observations on both grounds of magnitude, seasonal cycle, trend, and variance. Rainfall seasonality was characterized by pronounced spatial gradients with higher rainfall magnitudes and longer seasonal durations over the Lake Victoria basin (LVB) and western highlands but persistently shorter and drier seasons in northeastern Uganda. The first rainfall season (S1) typically begins earlier (late February) in southern and western regions and later in the north, while the second rainfall season (S2) generally exhibits earlier onset (early August) and later cessation across northern and western Uganda. Trend analysis indicates a widespread shortening (∼2 days per season) and weakening (∼15 mm per season) of the S1 season, contrasted by a lengthening (∼3 days per season) and intensification (∼20 mm per season) of S2 rainfall, particularly in northern and eastern regions. Both seasons were variedly characterized by above-normal and below-normal episodes with occurrence probabilities in the range of 5%–35%. Interseasonal comparisons further demonstrate S2’s dominant contribution especially over northern and western Uganda, whereas S1 remains dominant in the LVB. Finally, El Niño–Southern Oscillation (ENSO) and Indian Ocean dipoles (IODs) had strong but varied influences on seasonal nonexceedances and exceedances. The findings indicate a shift in Uganda’s seasonal rainfall structure with important implications for agricultural planning, water resource management, and climate adaptation strategies. Thus, we provide a comprehensive and high-resolution assessment of Uganda’s rainfall seasonality using updated CHIRPS3 data, offering crucial insights for climate resilience planning and sustainable development.