Shifting baselines alter trends and emergence of climate extremes across Africa

World Meteorological Organization baselines used to identify climate extremes are routinely updated to reflect recent climate conditions. Yet the implications of these updates for the characterization, trends, and detectability of climate extremes remain poorly understood, particularly in data-sparse and highly vulnerable regions such as Africa. Here, we quantify how updating the reference period from 1981 to 2010 to 1991–2020 systematically alters the characterization of temperature and precipitation extremes across the continent. Using multiple observational and reanalysis datasets (BEST, ERA5, MERRA-2, CHIRPS), we assess the sensitivity of percentile-based thresholds, long-term trends, and the Time of Emergence (ToE) to changes in the reference period. ToE is employed here as a diagnostic of detectability rather than a definitive marker of anthropogenic signal onset. Our results show that the updated baseline leads to higher temperature thresholds, resulting in a reduced frequency and slower trends for warm extremes (TX90p, TN90p), and a concurrent increase in cold extremes (TX10p, TN10p). Precipitation extremes exhibit more heterogeneous and dataset-dependent responses: trends in extreme precipitation totals (R95pTOT, R99pTOT) generally weaken, whereas intensity-based metrics (R95pINT, R99pINT) often strengthen, particularly in MERRA-2. Moreover, the choice of baseline strongly influences the estimated ToE. Warm extremes emerge 2–8 years later under the newer baseline, while cold extremes emerge earlier (by up to 15 years) due to enhanced signal-to-noise ratios. For precipitation, ToE responses vary widely across datasets and regions. In CHIRPS, the ToE of intense rainfall events is delayed, whereas in MERRA-2 it advances by over 2 decades in some regions. These results indicate that ToE estimates derived from recent decades are highly sensitive to baseline selection. By explicitly isolating the effect of baseline choice, this study provides a critical framework for interpreting extremes, reconciling dataset discrepancies, and improving the robustness of climate monitoring and risk communication across Africa. • Baseline shift raises temperature thresholds and alter the frequency extreme-heat. • Warm extremes emerge later, while cold extremes emerge much earlier. • Totals rainfall weakens but intensity-based extremes strengthen in many datasets. • Precipitation time-of-emergence responses diverge sharply across datasets.