Significantly wetter or drier future conditions for one to two thirds of the world's population

Future projections of precipitation are uncertain, hampering effective climate adaptation strategies globally. Our understanding of changes across multiple climate model simulations under a warmer climate is limited by this lack of coherence across models. Here, we address this challenge introducing an approach that detects agreement in drier and wetter conditions by evaluating continuous 120-year time-series with trends, across 146 Global Climate Model (GCM) runs and two elevated greenhouse gas (GHG) emissions scenarios. We show the hotspots of future drier and wetter conditions, including regions already experiencing water scarcity or excess. These patterns are projected to impact a significant portion of the global population, with approximately 3 billion people (38% of the world's current population) affected under an intermediate emissions scenario and 5 billion people (66% of the world population) under a high emissions scenario by the century's end (or 35–61% using projections of future population). We undertake a country- and state-level analysis quantifying the population exposed to significant changes in precipitation regimes, offering a robust framework for assessing multiple climate projections. Precipitation as the primary freshwater source, plays a crucial role in Earth's water availability1,2. Understanding changes in precipitation patterns under elevated greenhouse gas (GHG) emissions and their impact on the global population is essential for developing effective adaptation and mitigation strategies and preparing for increased natural disasters3. While temperature projections show general agreement, precipitation projections exhibit significant regional uncertainties4 and lack coherence among climate models10. Precipitation is complex to simulate due to various influencing factors, including diverse physics represented by global climate models (GCMs), their sensitivity to radiative forcing, rate of warming12,13 and to aerosols radiative cooling14,15. Sea surface temperature (SST) also plays a pivotal role in precipitation variability16 with a series of regional water deficits17,18 and surpluses2,19,20 often associated with specific SST patterns21. Unforced, internal climate fluctuations operating at timescales varying from intra-seasonal to multi-decadal additionally contribute to precipitation variability, e.g., the El Niño-Southern Oscillation22, the Indian Ocean Dipole23, the Pacific Decadal Oscillation24, the Southern25 and Northern26 Annular Modes, and the Atlantic Multi-decadal Oscillation27. Future GCM projections of multiple climate modes, their interactions, and resultant teleconnections with precipitation do not align over time across GCMs, amplifying the heterogeneity of projections11. As climate modelling science progresses, the number of ensembles—i.e., climate modelling experiments representative of physical processes, scenarios, and internally generated climate variability—increases along with computational power. This, however, expands the spread of the climate change signal of precipitation and increases uncertainty. To reconcile the wide range of precipitation projections from multiple GCMs, new approaches are needed28,29. Temporal aggregations, although useful for most climate metrics, are inadequate for heterogeneous variables like precipitation. Excessive temporal averaging (e.g., 10+ years) does not retain critical information and may obscure insights into the projected direction of significant changes. To address this issue, we present a novel approach that analyses trends in continuous, long-term time-series30,31 from multiple GCM ensembles and quantifies the agreement of wetter or drier conditions in terms of precipitation. Our study aims to detect global warming-induced wetting and drying patterns, understand differences between GCM generations, determine seasonal dominance, and identify "hotspots" of drier and wetter conditions with potential global human impacts. While drier and wetter conditions can also have broader definitions associated with atmospheric demand and characteristics of precipitation, here we focus on precipitation totals alone, which is the most important component and with greater uncertainty. We define global warming-induced drying or wetting as statistically significant and substantial continuous decreases or increases in precipitation capable of altering local regimes under intermediate and high emission scenarios. By using non-parametric trends30,31 and considering an ensemble of 146 CMIP53 and CMIP64 climate model runs (Tables S1 and S2), we identify regions where wetting and drying patterns converge across the globe. Our trend-based approach aligns with the continuous nature of radiative forcing, provides flexibility and robustness in detecting and quantifying global warming-induced changes, and effectively controls for natural variability32. Our innovative approach (see "Methods") evaluates the entire time-series using no interannual averaging and combines information from the fullest range of GCM projections available to determine their agreement and the extent of precipitation impacts with no ensemble aggregation. This is important because each set of simulations provides a plausible storyline of future precipitation patterns under elevated global warming, rather than using aggregated data which can obscure important trends and patterns. The approach also offers an impact-based framework, with country-scale analysis of the drying and wetting agreement, the magnitude of change, and the exposed population, now available to inform climate adaptation policies globally. Our study advances the understanding of how increased GHG emissions are likely to affect precipitation regimes and impact populations globally33, providing valuable insights into the direction of precipitation changes under different emissions scenarios and enhancing our ability to develop effective adaptation strategies. By addressing the lack of coherence in precipitation projections, our research contributes to a more comprehensive understanding of the future impacts of climate change on water resources.