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The idea that tropical forest and savanna are alternative states is crucial to how we manage these biomes and predict their future under global change. Large-scale empirical evidence for alternative stable states is limited, however, and comes mostly from the multimodal distribution of structural aspects of vegetation. These approaches have been criticized, as structure alone cannot separate out wetter savannas from drier forests for example, and there are also technical challenges to mapping vegetation structure in unbiased ways. Here, we develop an alternative approach to delimit the climatic envelope of the two biomes in Africa using tree species lists gathered for a large number of forest and savanna sites distributed across the continent. Our analyses confirm extensive climatic overlap of forest and savanna, supporting the alternative stable states hypothesis for Africa, and this result is corroborated by paleoecological evidence. Further, we find the two biomes to have highly divergent tree species compositions and to represent alternative compositional states. This allowed us to classify tree species as forest vs. savanna specialists, with some generalist species that span both biomes. In conjunction with georeferenced herbarium records, we mapped the forest and savanna distributions across Africa and quantified their environmental limits, which are primarily related to precipitation and seasonality, with a secondary contribution of fire. These results are important for the ongoing efforts to restore African ecosystems, which depend on accurate biome maps to set appropriate targets for the restored states but also provide empirical evidence for broad-scale bistability.

Precipitation exhibits a pronounced seasonal cycle, of which the phase and amplitude are closely associated with water resource management. While previous studies suggested an emerged delaying phase in the past decades, whether the amplified amplitude has emerged is controversial. Using multiple observational data sets and climate simulations, here we show that the amplification of precipitation annual cycle has emerged in most global land areas since the 1980s, especially in the tropics. These amplifications are mainly driven by anthropogenic emissions, and will be further intensified by 17.6% in the future (2081–2100) under high emission scenario (Shared Socioeconomic Pathways, SSP585), and limited to 7.2% under SSP126 scenario, relative to the historical period (1982–2014). Precipitation seasonality will be amplified by 4.2% for each 1°C of global warming, which is seen in all emission scenarios. The mitigation of lower emissions is helpful for alleviating the amplification of precipitation seasonality in a warming world. Plain Language Summary Precipitation displays pronounced seasonal cycle, and its phase and amplitude are closely associated with ecosystems and our society by redistributing water resources. The phase of precipitation cycle has been well understood in previous studies, but how its magnitude changes remain largely unknown. In this study, we use multiple observational data sets and climate simulations to show that precipitation annual cycle has been amplified in most parts of global land area since the 1980s. These amplifications are especially strong in the tropical regions, and are mainly driven by the increases in anthropogenic greenhouse gas and aerosol emissions. In the future (2081–2100) under high emission scenario (SSP585), they will be further intensified by 17.6% relative to the historical period (1982–2014), and will be limited to 7.2% under low emission scenario (SSP126). We also estimate that the amplitude of precipitation seasonality will be increased by around 4.2% for each 1°C of global warming, and suggest that keeping lower emissions is helpful for alleviating the amplification of precipitation seasonality. Key Points Precipitation annual cycle has been amplified in most global land areas since the 1980s, especially in the tropics Precipitation seasonality amplification will be intensified in the future, mainly driven by anthropogenic emissions The amplitude of precipitation seasonality will be amplified by ∼4.2% for each 1°C of global warming

Changes in precipitation seasonality or redistribution of precipitation could exert significant influences on regional water resources availability and the well-being of the ecosystem. However, due to the nonuniform distribution of precipitation stations and intermittency of precipitation, precise detection of changes in precipitation seasonality on the global scale is absent. This study identifies and inter-compares trends in precipitation seasonality within seven precipitation datasets during the past three decades, including two gauge-based datasets derived from the Climatic Research Unit (CRU) and the Global Precipitation Climatology Centre (GPCC), one remote sensing-retrieval obtained from Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), three reanalysis datasets obtained from National Centers for Environmental Prediction reanalysis II (NCEP2), European Centre for Medium-Range Weather Forecasts Interim Reanalysis (ERA-Interim), and Modern Era Reanalysis for Research and Applications Version 2 (MERRA2), and one precipitation dataset merged from above three types, Multi-Source Weighted Ensemble Precipitation Version 1.2 (MSWEP_V1.2). Values of two indices representing the precipitation seasonality, the normal seasonality index (SI) and the dimensionless seasonality index (DSI), are estimated for each land grid in each precipitation dataset. The results show that DSI is more sensitive to changes in the temporal distribution of precipitation as it considers both annual amount and monthly fluctuations of precipitation, compared to SI that only considers monthly fluctuations of precipitation. There are large differences in precipitation seasonality at annual and climatologic scales between precipitation datasets for both SI and DSI. Within the seven precipitation datasets, PERSIANN-CDR SI and DSI show high precipitation seasonality while CRU SI, and ERA-Interim and MERRA2 DSI show the low precipitation seasonality in all continental regions. During 1988–2013, PERSIANN-CDR, NCEP2 and ERA-Interim show more widespread, statistically significant trends in precipitation seasonality than other four precipitation datasets. PERSIANN-CDR and NCEP2 show statistically significant decreases in SI over Middle East and Central Asia, while ERA-Interim, MERRA2 and MSWEP_V1.2 SI increase over Central and South Africa. Increases in SI over the most of South America are significant. Regions of Canada/Greenland/Iceland, East and South Africa show significant increases in precipitation seasonality, while South Europe/Mediterranean and Central Africa show significant decreases in precipitation seasonality in most datasets. Although time series of seasonality indices values fluctuate correlatively in recent three decades, there are no regions on which all precipitation datasets show a consistent, statistically significant, positive or negative trend in indices of precipitation seasonality. These inconsistent changes in precipitation seasonality within various precipitation datasets imply the importance of choosing dataset when studying changes in regional precipitation seasonality.

1. The environmental filtering hypothesis predicts that the abiotic environment selects species withsimilar trait values within communities. Testing this hypothesis along multiple – and interacting –gradients of climate and soil variables constitutes a great opportunity to better understand and predictthe responses of plant communities to ongoing environmental changes.2. Based on two key plant traits, maximum plant height and specific leaf area (SLA), we assessedthe filtering effects of climate (mean annual temperature and precipitation, precipitation seasonality),soil characteristics (soil pH, sand content and total phosphorus) and all potential interactions on thefunctional structure and diversity of 124 dryland communities spread over the globe. The functionalstructure and diversity of dryland communities were quantified using the mean, variance, skewnessand kurtosis of plant trait distributions.3. The models accurately explained the observed variations in functional trait diversity across the124 communities studied. All models included interactions among factors, i.e. climate–climate (9%of explanatory power), climate–soil (24% of explanatory power) and soil–soil interactions (5% ofexplanatory power). Precipitation seasonality was the main driver of maximum plant height, andinteracted with mean annual temperature and precipitation. Soil pH mediated the filtering effects ofclimate and sand content on SLA. Our results also revealed that communities characterized by a lowvariance can also exhibit low kurtosis values, indicating that functionally contrasting species canco-occur even in communities with narrow ranges of trait values.4. Synthesis. We identified the particular set of conditions under which the environmental filteringhypothesis operates in drylands world-wide. Our findings also indicate that species with functionallycontrasting strategies can still co-occur locally, even under prevailing environmental filtering. Interactionsbetween sources of environmental stress should be therefore included in global trait-basedstudies, as this will help to further anticipate where the effects of environmental filtering will impactplant trait diversity under climate change.

During the 1930s Dust Bowl drought in the central United States, species with the C₃ photosynthetic pathway expanded throughout C₄-dominated grasslands. This widespread increase in C₃ grasses during a decade of low rainfall and high temperatures is inconsistent with well-known traits of C₃ vs. C₄ pathways. Indeed, water use efficiency is generally lower, and photosynthesis is more sensitive to high temperatures in C₃ than C₄ species, consistent with the predominant distribution of C₃ grasslands in cooler environments and at higher latitudes globally. We experimentally imposed extreme drought for 4 y in mixed C₃/C₄ grasslands in Kansas and Wyoming and, similar to Dust Bowl observations, also documented three- to fivefold increases in C₃/C₄ biomass ratios. To explain these paradoxical responses, we first analyzed long-term climate records to show that under nominal conditions in the central United States, C₄ grasses dominate where precipitation and air temperature are strongly related (warmest months are wettest months). In contrast, C₃ grasses flourish where precipitation inputs are less strongly coupled to warm temperatures. We then show that during extreme drought years, precipitation–temperature relationships weaken, and the proportion of precipitation falling during cooler months increases. This shift in precipitation seasonality provides a mechanism for C₃ grasses to respond positively to multiyear drought, resolving the Dust Bowl paradox. Grasslands are globally important biomes and increasingly vulnerable to direct effects of climate extremes. Our findings highlight how extreme drought can indirectly alter precipitation seasonality and shift ecosystem phenology, affecting function in ways not predictable from key traits of C₃ and C₄ species.

The wet season is commonly defined based on daily precipitation accumulation, which represents water inputs but does not account for losses from evaporation, infiltration, and runoff. Here, we estimate root‐zone soil moisture using observations from the Soil Moisture Active Passive (SMAP) satellite to capture year‐to‐year variations in seasonal soil moisture availability across Africa from 2016 to 2023 using a cumulative anomaly algorithm. Our analysis shows that seasonal soil moisture timing correlates more strongly (p < ${< } $ 0.01) with seasonal vegetation timing than precipitation across African croplands with over 30% crop cover. Additionally, soil moisture‐based onsets capture small early season rainfall events that precipitation‐based methods misclassify as false onsets. However, in Southern Hemisphere woodlands, neither soil moisture nor precipitation fully explains vegetation variability, likely due to deep‐rooted trees accessing moisture beyond SMAP's detection limits. These findings highlight soil moisture as a valuable indicator for refining wet season definitions, particularly in agricultural regions.

Central Asia is the world’s largest azonal arid region, with strong seasonal precipitation patterns. Vegetation in this region is relatively sparse and extremely sensitive to climatic changes. However, long-term trends in vegetation in Central Asia are still unclear or even controversially recognized, hindering the assessment of climate change’s impact on regional sustainability. Here, we present the longest time series of vegetation index in Central Asia and investigated its response to precipitation seasonality from 1982 to 2022 by integrating normalized difference vegetation index data from the Global Inventory Monitoring and Modeling Studies and the Moderate Resolution Imaging Spectroradiometer. The results indicate a greening trend during 1982–2000 and a browning trend during 2000–2008. In contrast to previous studies, we detected a rapid greening trend during 2008–2022, largely resulted from a continuous warm-wet trend in Central Asia. In addition, strong spatial variation in vegetation is uncovered within the region, suggesting spatial differences in vegetation responding to contrasting precipitation seasonality. Under CMIP6 climate scenarios, spring wetting and summer drying are projected to prompt Central Asian vegetation change to a simultaneous greening south and browning north.

Increased nitrogen deposition (N factor) and changes in precipitation patterns (W factor) can greatly impact soil microbial communities in tropical/subtropical forests. Although knowledge about the effects of a single factor on soil microbial communities is growing rapidly, little is understood about the interactive effects of these two environmental change factors. In this study, we investigated the responses of soil bacterial and fungal communities to the short-term simulated environmental changes (nitrogen addition, precipitation seasonality change, and their combination) in a subtropical forest in South China. The interaction between N and W factors was detected significant for affecting some soil physicochemical properties (such as pH, soil water, and NO 3 - contents). Fungi were more susceptible to treatment than bacteria in a variety of community traits (alpha, beta diversity, and network topological features). The N and W factors act antagonistically to affect fungal alpha diversity, and the interaction effect was detected significant for the dry season. The topological features of the meta-community (containing both bacteria and fungi) network overrode the alpha and beta diversity of bacterial or fungal communities in explaining the variation of soil enzyme activities. The associations between Ascomycota fungi and Gammaproteobacteria or Alphaproteobacteria might be important in mediating the inter-kingdom interactions. In summary, our results suggested that fungal communities were more sensitive to N and W factors (and their interaction) than bacterial communities, and the treatments’ effects were more prominent in the dry season, which may have great consequences in soil processes and ecosystem functions in subtropical forests.

Meteorological stations are rare in central Africa, which leads to uncertainty in regional climatic trends. This is particularly problematic for the Congo Basin, where station coverage decreased significantly during the last few decades. Here, we present a digitized dataset of daily temperature and precipitation from the Yangambi biosphere reserve, covering the period 1960–2020 (61 years) and located in the heart of the Congo Basin. Our results confirm a long-term increase in temperature and temperature extremes since the 1960s, with strong upward trends since the early 1990s. Our results also indicate a drying trend for the dry season and intensification of the wet season since the early 2000s. Ongoing warming and increasing precipitation seasonality and intensity already have a significant impact on crop yields in Yangambi. This calls for urgent development of climate-smart and dynamic agriculture and agroforestry systems. We conclude that systematic digitization and climate recording in the Congo Basin will be critical to improve much-needed gridded benchmark datasets of climatic variables.

The Mediterranean climate is principally characterized by warm, dry summers and cool, wet winters. However, there are large variations in precipitation dynamics in regions with this climate type. We examined the variability of precipitation within and among Mediterranean-climate areas, and classified the Mediterranean climate as wet, moderate, or dry based on annual precipitation; and strongly, moderately, or weakly seasonal based on percentage of precipitation during summer. Mediterranean biomes are mostly dry (<700 mm annually) but some areas are wet (>1300 mm annually); and many areas are weakly seasonal (>12% of annual precipitation during summer). We also used NOAA NCDC climate records to characterize interannual variability of annual and dry-season precipitation, as well as trends in annual, winter, and dry-season precipitation for 337 sites that met the data quality criteria from 1975 to 2015. Most significantly, sites in many Mediterranean-climate regions show downward trends in annual precipitation (southern California, Spain, Australia, Chile, and Northern Italy); and most of North America, the Mediterranean basin, and Chile showed downward trends in summer precipitation. Variations in annual and summer precipitation likely contribute to the high biodiversity and endemism characteristic of Mediterranean-climate biomes; the data indicate trends toward harsher conditions over the past 40 years.