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Since 2010 Easter Island (Rapa Nui) has experienced a severe, decadal‐scale megadrought. Observations show that every year from 2010 to 2023 had lower precipitation than the 1979–2009 average. The decline in precipitation aligns with decadal‐scale climate shifts: an intensification and westward expansion of the South Pacific Anticyclone toward Rapa Nui, along with a poleward shift of the Southern Hemisphere storm track. Both the trends and interannual variability in these phenomena closely track the hydroclimate of Rapa Nui and explain why fewer storms are reaching the island. We show that these dynamical and aridification trends have likely already exceeded natural variability and that they will continue through the 21st century under an intermediate greenhouse‐gas emissions scenario. We attribute approximately 70%–80% of the current megadrought severity to anthropogenic climate change, under which even more severe droughts are projected to occur.

Large tropical volcanic eruptions can affect the climate of many regions on Earth, yet it is uncertain how the largest eruptions over the past millennium may have altered Earth’s hydroclimate. Here, we analyze the global hydroclimatic response to all the tropical volcanic eruptions over the past millennium that were larger than the Mount Pinatubo eruption of 1991. Using the Paleo Hydrodynamics Data Assimilation product (PHYDA), we find that these large volcanic eruptions tended to produce dry conditions over tropical Africa, Central Asia and the Middle East and wet conditions over much of Oceania and the South American monsoon region. These anomalies are statistically significant, and they persisted for more than a decade in some regions. The persistence of the anomalies is associated with southward shifts in the Intertropical Convergence Zone and sea surface temperature changes in the Pacific and Atlantic oceans. We compare the PHYDA results with the stand-alone model response of the Community Earth System Model (CESM)-Last Millennium Ensemble. We find that the proxy-constrained PHYDA estimates are larger and more persistent than the responses simulated by CESM. Understanding which of these estimates is more realistic is critical for accurately characterizing the hydroclimate risks of future volcanic eruptions.

Hydroclimate extremes critically affect human and natural systems, but there remain many unanswered questions about their causes and how to interpret their dynamics in the past and in climate change projections. These uncertainties are due, in part, to the lack of long-term, spatially resolved hydroclimate reconstructions and information on the underlying physical drivers for many regions. Here we present the first global reconstructions of hydroclimate and associated climate dynamical variables over the past two thousand years. We use a data assimilation approach tailored to reconstruct hydroclimate that optimally combines 2,978 paleoclimate proxy-data time series with the physical constraints of an atmosphere--ocean climate model. The global reconstructions are annually or seasonally resolved and include two spatiotemporal drought indices, near-surface air temperature, an index of North Atlantic variability, the location of the intertropical convergence zone, and monthly Niño indices. This database, called the Paleo Hydrodynamics Data Assimilation product (PHYDA), will provide a critical new platform for investigating the causes of past climate variability and extremes, while informing interpretations of future hydroclimate projections.

The climate of the Southern Hemisphere (SH) is strongly influenced by variations in the El Niño–Southern Oscillation (ENSO) and the Southern Annular Mode (SAM). Because of the limited length of instrumental records in most parts of the SH, very little is known about the relationship between these two key modes of variability over time. Using proxy-based reconstructions and last-millennium climate model simulations, we find that ENSO and SAM indices are mostly negatively correlated over the past millennium. Pseudo-proxy experiments indicate that currently available proxy records are able to reliably capture ENSO–SAM relationships back to at least 1600 CE. Palaeoclimate reconstructions show mostly negative correlations back to about 1400 CE. An ensemble of last-millennium climate model simulations confirms this negative correlation, showing a stable correlation of approximately −0.3. Despite this generally negative relationship we do find intermittent periods of positive ENSO–SAM correlations in individual model simulations and in the palaeoclimate reconstructions. We do not find evidence that these relationship fluctuations are caused by exogenous forcing nor by a consistent climate pattern. However, we do find evidence that strong negative correlations are associated with strong positive (negative) anomalies in the Interdecadal Pacific Oscillation and the Amundsen Sea Low during periods when SAM and ENSO indices are of opposite (equal) sign.

Geological evidence from the last millennium indicates that multidecadal megadroughts may have occurred simultaneously in California and Patagonia at least once. However, it is unclear whether or not megadroughts were common in South America, whether or not simultaneous megadroughts in North and South America occurred repeatedly, and what would cause their simultaneous occurrence. Here we use a data-assimilation-based global hydroclimate reconstruction, which integrates palaeoclimate records with constraints from a climate model, to show that there were about a dozen megadroughts in the South American Southwest over the last millennium. Using dynamical variables from the hydroclimate reconstruction, we show that these megadroughts were driven by the El Niño/Southern Oscillation (ENSO). We also find that North American Southwest and South American Southwest megadroughts have occurred simultaneously more often than expected by chance. These coincident megadroughts were driven by an increased frequency of cold ENSO states relative to the last millennium-average frequency. Our results establish the substantial risk that exists for ENSO-driven, coupled megadroughts in two critical agricultural regions. Cold ENSO states can lead to the simultaneous occurrence of megadroughts in southwestern North and South America, according to a hydroclimate reconstruction of the last thousand years assimilating palaeoclimate records with climate model constraints.

Mississippi River basin floods impart large socioeconomic impacts over the central United States. Improving flood predictability depends on our understanding of the dynamical controls on Mississippi basin hydroclimate. However, short instrumental records make it difficult to constrain the connections between flooding and climate variability. Here, we use the Paleo Hydrodynamics Data Assimilation product, spanning the Last Millennium, to investigate the impacts of tropical Pacific and North Atlantic sea surface temperature (SST) variability on hydrological extremes across the Mississippi River and its major tributaries. Wet extremes are associated with strong El Niño‐like warming over the tropical Pacific, but specific SST patterns matter: dry (wet) conditions occur during Central Pacific (Eastern Pacific) El Niño events. The influence of North Atlantic SSTs is less clear, but cool SSTs contribute to Ohio basin wet extremes. These results are relevant for seasonal‐to‐interannual flood hazard prediction on the fourth largest river basin in the world. Plain Language Summary Large floods on the Mississippi River during the last century have proven costly, both in economic and social terms. To improve flood hazard prediction, it is critical to understand how patterns in Earth's climate system contribute to Mississippi floods. However, relatively few flooding events occurred during the 20th century, which limits our ability to evaluate the statistics of these events. This work seeks to use records of past climate (from archives such as corals, ice cores, and trees) spanning the last 1,000 years to investigate the drivers of Mississippi flooding. Specifically, we focus on how sea surface temperature (SST) changes over the tropical Pacific and North Atlantic affect the hydrological cycle over the basin. We find that very wet periods are strongly associated with warm SSTs in the eastern tropical Pacific, but central tropical Pacific warming causes dry conditions. The impact of North Atlantic SST variability is much weaker, but, in combination with tropical Pacific warming, cool SSTs over the North Atlantic can amplify wet conditions over the eastern Mississippi River basin's tributaries. Our results harbor implications for seasonal‐to‐interannual flood hazard prediction, and can help stakeholders prepare for and mitigate flooding in the 21st century. Key Points We investigate the drivers of Mississippi River basin hydroclimate extremes over the last millennium Paleoclimate data assimilation reveals dry (wet) conditions during central Pacific (eastern Pacific) El Niño events Self‐organizing maps expose a North Atlantic tripole pattern which modulates moisture supply over the Ohio river basin

The efficacy of a novel ensemble data assimilation (DA) technique is examined in the climate field reconstruction (CFR) of surface temperature. A minimalistic, computationally inexpensive DA technique is employed that requires only a static ensemble of climatologically plausible states. Pseudoproxy experiments are performed with both general circulation model (GCM) and Twentieth Century Reanalysis (20CR) data by reconstructing surface temperature fields from a sparse network of noisy pseudoproxies. The DA approach is compared to a conventional CFR approach based on principal component analysis (PCA) for experiments on global domains. DA outperforms PCA in reconstructing global-mean temperature in all experiments and is more consistent across experiments, with a range of time series correlations of 0.69–0.94 compared to 0.19–0.87 for the PCA method. DA improvements are even more evident in spatial reconstruction skill, especially in sparsely sampled pseudoproxy regions and for 20CR experiments. It is hypothesized that DA improves spatial reconstructions because it relies on coherent, spatially local temperature patterns, which remain robust even when glacial states are used to reconstruct nonglacial states and vice versa. These local relationships, as utilized by DA, appear to be more robust than the orthogonal patterns of variability utilized by PCA. Comparing results for GCM and 20CR data indicates that pseudoproxy experiments that rely solely on GCM data may give a false impression of reconstruction skill.

Paleoclimatic records provide valuable information about Holocene climate, revealing aspects of climate variability for a multitude of sites around the world. However, such data also possess limitations. Proxy networks are spatially uneven, seasonally biased, uncertain in time, and present a variety of challenges when used in concert to illustrate the complex variations of past climate. Paleoclimatic data assimilation provides one approach to reconstructing past climate that can account for the diverse nature of proxy records while maintaining the physics-based covariance structures simulated by climate models. Here, we use paleoclimate data assimilation to create a spatially complete reconstruction of temperature over the past 12 000 years using proxy data from the Temperature 12k database and output from transient climate model simulations. Following the last glacial period, the reconstruction shows Holocene temperatures warming to a peak near 6400 years ago followed by a slow cooling toward the present day, supporting a mid-Holocene which is at least as warm as the preindustrial. Sensitivity tests show that if proxies have an overlooked summer bias, some apparent mid-Holocene warmth could actually represent summer trends rather than annual mean trends. Regardless, the potential effects of proxy seasonal biases are insufficient to align the reconstructed global mean temperature with the warming trends seen in transient model simulations.

Hot and moist “hothouse” climates occurred in Earth's past and are expected in Earth's far future climate, driven by increasing solar luminosity. In hothouse climate regimes, precipitation transitions from a quasi‐steady state, as in present‐day tropical convection, to an “episodic deluge” or relaxation‐oscillator (RO) regime where precipitation occurs in intense bursts separated by multi‐day dry spells. Recent studies suggest that the transition to RO convection regimes is radiatively driven. However, the transition from steady state to RO convection has only been studied with radiative convective equilibrium (RCE) simulations with constant insolation, excluding the diurnal cycle. Precipitation and convection are strongly linked to the diurnal cycle in Earth's present climate over both land and ocean. We explore the impact of the diurnal cycle on the transition from steady state to RO convection using two sets of small‐domain RCE simulations with ocean and swamp‐like surface boundary conditions. Our RCE simulations with ocean boundary conditions show convection transitions to an episodic deluge regime at 322 K and the diurnal cycle modulates precipitation to occur during late‐night or near dawn, when convective inhibition is the weakest. Our RCE simulations with swamp‐like boundary conditions, which allow for mean surface temperature variations, show that as RO states emerge, the diurnal cycle modulates precipitation to primarily occur during the late‐afternoon to about dusk; but as the mean SST increases, precipitation occurs during the late‐night to dawn. These results show that the diurnal cycle strongly influences the timing of convection and precipitation patterns in extreme climates. In hot and wet “hothouse” climate conditions, rainfall transitions from a pattern that fluctuates from about a mean of 3 mm to more intense outbursts that are separated by multi‐day dry spells. Previous studies on hothouse climates did not consider the role of the diurnal cycle even though it strongly controls precipitation in Earth's current climate. This study uses radiative‐convective equilibrium simulations to investigate the impact of rising temperatures on the transition to hothouse conditions, incorporating the diurnal cycle with both swamp‐like and open ocean surface conditions. We find that episodic precipitation occurs at surface temperatures above 322 K even when accounting for the diurnal cycle. However, the diurnal cycle significantly influences the timing of convection and rainfall at high temperatures with precipitation primarily starting late at night or in the early morning. We study hothouse climates using radiative convective equilibrium simulations with a diurnal cycle over ocean and swamp‐like conditions A transition from steady state to episodic precipitation occurs when accounting for diurnal variability at high surface temperatures The diurnal cycle modulates the episodic precipitation events with precipitation occurring primarily during the dawn or dusk hours

Model simulations and proxy-based reconstructions are the main tools for quantifying pre-instrumental climate variations. For some metrics such as Northern Hemisphere mean temperatures, there is remarkable agreement between models and reconstructions. For other diagnostics, such as the regional response to volcanic eruptions, or hemispheric temperature differences, substantial disagreements between data and models have been reported. Here, we assess the potential sources of these discrepancies by comparing 1000-year hemispheric temperature reconstructions based on real-world paleoclimate proxies with climate-model-based pseudoproxies. These pseudoproxy experiments (PPE) indicate that noise inherent in proxy records and the unequal spatial distribution of proxy data are the key factors in explaining the data-model differences. For example, lower inter-hemispheric correlations in reconstructions can be fully accounted for by these factors in the PPE. Noise and data sampling also partly explain the reduced amplitude of the response to external forcing in reconstructions compared to models. For other metrics, such as inter-hemispheric differences, some, although reduced, discrepancy remains. Our results suggest that improving proxy data quality and spatial coverage is the key factor to increase the quality of future climate reconstructions, while the total number of proxy records and reconstruction methodology play a smaller role.