Sea Surface Temperature Research Articles

Impact of North Atlantic Tripole and Extratropical North Pacific Extreme SSTs on the 2023/24 El Niño

AbstractObservations revealed notable discrepancies in the 2023/24 El Niño compared to earlier events, despite registering moderate Niño3.4 index magnitudes. Essential indicators such as the westerly wind burst, thermocline zonal tilting, and eastward propagation of oceanic Kelvin waves were conspicuously weak, indicating a weak air‐sea coupled in contrast to past occurrences, the 2023/24 El Niño coincided with unusually high North Atlantic Tripole and extratropical North Pacific sea surface temperatures (SSTs). The elevated North Atlantic Tripole SST triggered a strong negative Pacific meridional mode and easterly anomalies in the equatorial western‐central Pacific. At the same time, the extratropical North Pacific SST induced a negative Pacific Decadal Oscillation‐like pattern. These anomalies potentially dampened SST‐wind coupling during the developmental stages of El Niño. The negative Pacific meridional mode distinguished the 2023/24 El Niño from previous events and substantially altered its local and remote influences.

Sea surface wind retrieval from CFOSAT Rotating fan-beam scatterometer in ocean cyclones

ABSTRACT Rotating fan-beam scatterometer (RFSCAT) is a radar scatterometer system for sea surface wind vector measurement. Compared with other available scatterometers, RFSCAT can provide more combination of azimuth angles and incidence angles for a single WVC (wind vector cell), this observation mechanism is more conducive to the sea surface wind direction retrieval. In this paper, the NSCAT-4DS GMF (geophysical model function) with SST (sea surface temperature) correction, and the MSS (Multiple Solution Scheme) combination with a 2DVAR (2-dimensional variational analysis) are adopted to retrieve the sea surface winds from RFSCAT on the CFOSAT (Chinese-French Oceanography Satellite). The retrieved RFSCAT sea surface winds and ASCAT (advanced scatterometer) sea surface winds are compared and tested, and the feasibility of the RFSCAT measuring sea surface winds under high wind speeds is analysed. The results show that the RMSE of the RFSCAT sea surface wind speed using improved algorithm has decreased by 0.292 m s−1, the correlation coefficient has increased by 0.032, and the residual standard deviation has decreased by 0.194 m s−1. The RMSE of RFSCAT sea surface wind direction has decreased by 5.950°, the correlation coefficient has increased by 0.002, and the residual standard deviation has decreased by 5.567°. It is shown that the changes in winds RMSE using pre-improved and improved algorithms have statistical significance. In a word, the spaceborne Ku-band rotating fan-beam scatterometer can capture the winds structure of ocean cyclones. Although there may be high wind speeds saturation phenomena, the detecting sea surface winds at high wind speeds show preferable performance.

Global assessment of interannual variability in coastal urban areas and ecosystems

Abstract Both seasonal and extreme climate conditions are influenced by long-term natural internal variability. However, in general, long-term hazard variation has not been incorporated into coastal risk assessments. There are coastal regions of high interest, such as urban areas, where a large number of people are exposed to hydrometeorological hazards, and ecosystems, which provide protection, where long-term natural variability should be considered a design factor. In this study, we systematized climate analysis to identify high-interest regions where hazard long-term variability should be considered in risk assessment, disaster reduction, and future climate change adaptation and protection designs. To achieve this goal, we examined the effect of the leading modes of climate variability (Arctic Oscillation, Southern Annular Mode, and El Niño–Southern Oscillation) on the variation in the recurrence of extreme coastal hazard events, including as a first step sea surface temperature, winds, and waves. Neglecting long-term variability could potentially lead to the underperformance of solutions, or even irreversible damage that compromises the conditions of ecosystems for which nature-based solutions are designed.

Tree Rings Reveal ENSO in the Last Millennium

AbstractWe present new climate field reconstructions (CFR) of tropical Pacific ENSO sea surface temperatures (HadISST) for the boreal winter season using a circum‐Pacific tree‐ring network from known El Niño rainfall impact regions. We use two different CFR methods: Point‐by‐Point Regression (PPR) and reduced‐space Orthogonal Spatial Regression (OSR). Both methods produce reconstructions with high validation skill, but OSR is preferred because it has less spatial noise and is more efficient. Only the leading EOF of the SST field (EOF1) can be skillfully reconstructed by either method; EOF2 does not validate. The success of EOF1 reflects its importance for ENSO rainfall impacts over land; the failure with EOF2 is from the lack of these impacts. EOF1 allows for the reconstruction of many ENSO indices, including the ENSO Longitudinal Index (ELI). We also find evidence in our reconstructions for a recent increase in ENSO activity.

Forecasting crude oil prices with global ocean temperatures

This paper explores the role of ocean temperature (OT) in forecasting crude oil prices. Global OT shows statistically and economically significant predictive power, with corresponding in-sample and out-of-sample R2s of 1.81 % and 1.74 %, respectively. Both southern and northern hemisphere OTs can predict future oil prices, and the former is more predictive. Compared to the Atlantic, OTs in the Indian and Pacific Oceans display considerable predictive gains. In addition, we find that OTs have stronger forecasting power for crude oil prices in the recent period, with the in-sample R2 of global OT for the recent decade's sample reaching as high as 2.74 %. Correspondingly, we find that OTs' predictive power in the full sample can be explained through two channels: market risk and investor attention. In the recent period, in addition to these two channels, OTs have also influenced crude oil prices through the oil fundamental channel.

Signature of the western boundary currents in local climate variability.

The western boundary currents are characterized by narrow, intense ocean jets and are among the most energetic phenomena in the world ocean. The importance of the western boundary currents to the mean climate is well established: they transport vast quantities of heat from the subtropics to the midlatitudes1, and they govern the structure of the climatological mean surface winds2-6, precipitation4-6 and extratropical storm tracks7-13. Their importance to climate variability is much less clear, as the tropospheric response to extratropical sea surface temperature (SST) variability is generally modest relative to the internal variability in the midlatitude atmosphere12-14. Here we exploit novel local analyses based on high-spatial-resolution data to demonstrate that SST variability in the western boundary currents has a more robust signature in climate variability than has been indicated in previous work. Our results indicate that warm SST anomalies in the major boundary currents of both hemispheres are associated with a distinct signature of locally enhanced precipitation and rising motion anomalies that extend throughout the depth of the troposphere. The tropospheric signature closely mirrors that of ocean dynamical processes in the boundary currents. Thus, the findings indicate a distinct and robust pathway through which extratropical ocean dynamical processes influence local climate variability. The observational relationships are also reproducible in Earth system model simulations but only when the simulations are run at high spatial resolution.

Population genetic structure of Montipora digitata coral-algal symbiosis in the South China Sea

Species genetic diversity can reflect their adaptability to environmental changes. Coral reefs worldwide are in rapid decline due to climate change and human activities, highlighting the need for conservation intervention. The South China Sea (SCS) is an important biodiversity hotspot, particularly in the Xisha Islands (XS) that contain the majority of coral species in the SCS. However, few studies exist on coral genetic diversity in the SCS. Montipora digitata is the dominant reef-building coral species in the XS. In this study, two mitochondrial DNA fragments and 11 microsatellite markers were employed to investigate the genetic diversity of coral host of M. digitata, and ITS amplicon sequencing was employed to investigate the Symbiodiniaceae community structure. We sampled five wild populations of M. digitata in Hainan Island and the Xisha Islands. Montipora digitata showed low genetic diversity across populations in the SCS. We found significant genetic differentiation between the Sanya (SY) and XS populations but no significant genetic differentiation within XS populations. We identified four Symbiodiniaceae genera, with Cladocopium and Durusdinium being the most common, as well as Gerakladium and Fugacium. The Symbiodiniaceae types of SY and XS are significantly different, C15 is abundant in all populations, while D1a is also abundant in SY. We analyzed the correlation between 16 environmental factors and the genetic diversity of symbionts, genetic diversity of coral host and Symbiodiniaceae community was significantly correlated with sea surface temperature (SST) and other environmental factors. Our study provided a detailed paradigm from the perspective of genetic diversity of how a dominant coral species can be indicative of poor environmental adaptation potential.

Antarctic sea ice surface temperature bias in atmospheric reanalyses induced by the combined effects of sea ice and clouds

Sea-ice surface temperature from atmospheric reanalysis has been used as an indicator of ice melt and climate change. However, its performance in atmospheric reanalyses is not fully understood in Antarctica. Here, we quantified biases in six widely-used reanalyses using satellite observations, and found strong and persistent warm biases in most reanalyses examined. Further analysis of the biases revealed two main culprits: incorrect cloud properties, and inappropriate sea-ice representation in the reanalysis products. We found that overestimated cloud simulation can contribute more than 4 K warm bias, with ERA5 exhibiting the largest warm bias. Even in reanalysis with smaller biases, this accuracy is achieved through a compensatory relationship between relatively lower cloud fraction bias and overestimated sea ice insulation effect. A dynamic downscaling simulation shows that differences in sea-ice representation can contribute a 2.3 K warm bias. The representation of ice concentration is the primary driver of the spatial distribution of biases by modulating the coupling between sea ice and clouds, as well as surface heat conduction. The lack of a snow layer in all reanalyses examined also has an impact on biases.

Drying trend in land and sea in East Asia during the warm season over the past four decades

Abstract The East Asian region is typically characterized by warm and humid conditions from late spring to summer. However, in recent decades, this region has experienced an increase in severe drying conditions, deviating from historical climatological patterns. This study investigated the precipitation-evaporation (P-E) trends across land and sea regions in East Asia (EA) during the extended summer season (April to September) from 1980 to 2022, and the key physical processes driving these trends through moisture budget decomposition and numerical experiments. The results reveal pronounced drying trends in southeastern China and the Yellow Sea and parts of the Korea Strait and Korean Peninsula over the past 43 years. The underlying physical processes driving these drying conditions differ between land and sea in EA. In southeastern China, the drying is driven by dynamic processes, particularly moisture divergence related to wind changes. This is linked to anomalous strengthening of descending motion due to the Indo-Pacific warm pool warming induced by both anthropogenic global warming and natural Pacific Decadal Oscillation-like sea surface temperature (SST) patterns. Conversely, drying in the Yellow Sea and adjacent areas is influenced by thermodynamic moisture advection. The altered humidity distribution due to global warming-induced SST patterns, which are higher over the Northwest Pacific marginal sea and lower in inland China, drives dry air transport from inland China to the Yellow Sea via background southwesterly wind. These findings enhance our understanding of the drying trend and their distinct processes in EA’s land and sea areas during the extended summer.

Atmospheric variability drives anomalies in the Bering Sea air-sea heat exchange

Abstract High-latitudes, including the Bering Sea, are experiencing unprecedented rates of change. Long-term Bering Sea warming trends have been identified, and marine heatwaves (MHWs), event-scale elevated sea surface temperature (SST) extremes, have also increased in frequency and longevity in recent years. Recent work has shown that variability in air-sea coupling plays a dominant role in driving Bering Sea upper ocean thermal variability, and that surface forcing has driven an increase in the occurrence of positive ocean temperature anomalies since 2010. In this work, we characterize the drivers of the anomalous surface air-sea heat fluxes in the Bering Sea over the period 2010-2022 using ERA5 fields. We show that the surface turbulent heat flux dominates the net surface heat flux variability from September-April, and is primarily a result of near-surface air temperature and specific humidity anomalies. The air-mass anomalies that account for the majority of the turbulent heat flux variability are a function of wind direction, with southerly (northerly) wind advecting anomalously warm (cool), moist (dry) air over the Bering Sea, resulting in positive (negative) surface turbulent flux anomalies. During the remaining months of the year, anomalies in the surface radiative fluxes account for the majority of the net surface heat flux variability, and are a result of anomalous cloud coverage, anomalous lower tropospheric virtual temperature, and sea ice coverage variability. Our results indicate that atmospheric variability drives much of the Bering Sea upper ocean temperature variability through mediation of the surface heat fluxes during the analysis period.

Stability of cloud detection methods for Land Surface Temperature (LST) Climate Data Records (CDRs)

The stability of a climate data record (CDR) is essential for evaluating long-term trends in surface temperature using remote sensing products. In the case of a satellite-derived CDR of land surface temperature (LST), this includes the stability of processing steps prior to the estimation of the target climate variable. Instability in the masking of cloud-affected observations can result in non-geophysical trends in a LST CDR. This paper provides an assessment of cloud detection performance stability over a 25-year LST CDR generated using data from the second Along-Track Scanning Radiometer (ATSR-2), the Advanced Along-Track Scanning Radiometer (AATSR), the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Sea and Land Surface Temperature Radiometer (SLSTR). We evaluate three cloud detection methodologies, one fully Bayesian, one naïve probabilistic and the operational threshold-based cloud mask provided with each sensor, at four in-situ ceilometer sites. Of the 12 algorithm-site combinations assessed, only two (17 %) were stable across the full timeseries with respect to both cloud contamination and missed clear-sky observations. Five (42 %) were stable with respect to missed clear-sky observations only. The associated impacts on LST trends in the CDR could be as large as (+/−)0.73 K per decade (0.43 K per decade above the target stability), which means that attention needs to be paid to this aspect of stability in order to understand uncertainty in long-term observed trends. Given that cloud detection stability has not to our knowledge been previously assessed for any target climate variable, this conclusion may apply more broadly to other satellite-derived CDRs.

Structural and Dynamical Quality Assessment of Gap‐Filled Sea Surface Temperature Products

AbstractPrevious studies that intercompared global Level‐4 (L4) sea surface temperature (SST) analyses were centered on the assessment of the accuracy and bias of SST by comparing them with independent near‐surface Argo profile temperature data. This type of assessment is centered on the absolute value of SST rather than on SST spatial properties (gradients), which is more relevant to the study of oceanographic features (e.g., fronts, eddies, etc.) and ocean dynamics. Here, we use, for the first time, the spectrum of singularity exponents to assess the structural and dynamical quality of different L4 gap‐filled products based on the multifractal theory of turbulence. Singularity exponents represent the geometrical projection of the turbulence cascade, and its singular spectrum can be related to the probability density function of the singularity exponents normalized by the scale. Our results reveal that the different schemes used to produce the L4 SST products generate different singularity spectra, which are then used to identify a potential loss of dynamical information or structural coherence. This new diagnostic constitutes a valuable tool to assess the structural quality of SST products and can support data satellite SST producers efforts to improve the interpolation schemes used to generate gap‐filled SST products.

Comparing the atmospheric responses to reduced Arctic sea ice, a warmer ocean, and increased CO2 and their contributions to projected change at 2 °C global warming

Abstract We consider the combined and individual influences of Arctic sea-ice loss, sea surface temperature (SST) warming, and the direct radiative effect of increased CO2 on the Northern Hemispheric climate. The surface climate (e.g., temperature, precipitation) and atmospheric circulation responses (e.g., sea level pressure, wind) to these drivers are quantified using simulations from the Polar Amplification Model Intercomparison Project (for sea-ice loss and SST warming) and the Cloud Feedback Model Intercomparison Project (for increased CO2). We verify the linear additivity of the PAMIP-derived winter responses to sea-ice loss and SST change. The responses to SST change are of greater magnitude than that due to sea-ice loss or due to CO2 direct radiative forcing in most seasons and regions, excluding the Arctic. Notably however, sea-ice loss is at least as important as SST change for the winter atmospheric circulation response over the North Atlantic and Siberia. The dynamical responses to sea-ice loss and SST change oppose each other in many regions in winter, while the responses to SST and CO2 direct radiative forcing are often opposing in summer. Such opposing responses are less evident for the thermodynamical response. The sum of all three responses reproduces well the spatial patterns of change at 2 °C global warming in winter and autumn in the Coupled Model Intercomparison Project phase 6 projections, but overestimates their magnitude.

A coordination attention residual U-Net model for enhanced short and mid-term sea surface temperature prediction

Sea surface temperature (SST) is crucial for studying global oceans and evaluating ecosystems. Accurately predicting short and mid-term daily SST has been a significant challenge in oceanography. Traditional deep learning methods can handle temporal data and spatial features but often struggle with long-range spatiotemporal dependencies. To address this, we propose a coordination attention residual U-Net(CResU-Net) model designed to better capture the dynamic spatiotemporal correlations of high-resolution SST. The model integrates coordinate attention mechanisms, multiple residual modules, and depthwise separable convolutions to enhance prediction capabilities. The spatiotemporal variations of SST across different areas of the South China Sea are complex, making accurate predictions challenging. Experiments across various regions of the South China Sea show the model’s effectiveness and robust generalization in predicting high-resolution daily SST. For a 10-day forecast period, the model achieves approximately 0.3 °C in RMSE, outperforming several advanced models.

Long‐Term Variability of Phytoplankton Primary Production in the Ulleung Basin, East Sea/Japan Sea Using Ocean Color Remote Sensing

AbstractIn recent years, significant changes in environmental conditions and marine ecosystems have been observed in the East Sea/Japan Sea. This study investigates the long‐term environmental dynamics and phytoplankton responses in the Ulleung Basin, situated in the southwestern East Sea/Japan Sea, utilizing satellite and in situ data from 2002 to 2021. Over this period, there was a noticeable increase in sea surface temperature (SST) (r = 0.5739, p < 0.01), accompanied by decreasing mixed layer depth (MLD) and chlorophyll‐a (Chl‐a) concentration (r = −0.6193 and −0.6721, respectively; p < 0.01). Nutrient concentrations within the upper 50 m significantly declined for nitrate and phosphate. A reduction in the N:P ratio indicated a shift from phosphorus‐limited to nitrogen‐limited environment. Moreover, primary production (PP) demonstrated a decreasing trend (r = −0.5840, p < 0.01), coinciding with an increase in small phytoplankton contribution (r = 0.6399, p < 0.01). Rising SST potentially altered the water column's vertical structure, hindering nutrient entrainment from the deep ocean. Consequently, this nutrient limitation may increase small phytoplankton contribution, resulting in a decline in total PP. Under the IPCC's SSP5‐8.5 scenario, small phytoplankton contribution in the Ulleung Basin is projected to rise by over 10%, resulting in a 29% average PP decrease by 2100. This suggests a diminishing energy supply to the food web in a warming ocean, impacting higher trophic levels and major fishery resources. These findings emphasize the critical need for understanding and monitoring these environmental shifts for effective fisheries management and marine ecosystem conservation.

Body size and early marine conditions drive changes in Chinook salmon productivity across northern latitude ecosystems.

Disentangling the influences of climate change from other stressors affecting the population dynamics of aquatic species is particularly pressing for northern latitude ecosystems, where climate-driven warming is occurring faster than the global average. Chinook salmon (Oncorhynchus tshawytscha) in the Yukon-Kuskokwim (YK) region occupy the northern extent of their species' range and are experiencing prolonged declines in abundance resulting in fisheries closures and impacts to the well-being of Indigenous people and local communities. These declines have been associated with physical (e.g., temperature, streamflow) and biological (e.g., body size, competition) conditions, but uncertainty remains about the relative influence of these drivers on productivity across populations and how salmon-environment relationships vary across watersheds. To fill these knowledge gaps, we estimated the effects of marine and freshwater environmental indicators, body size, and indices of competition, on the productivity (adult returns-per-spawner) of 26 Chinook salmon populations in the YK region using a Bayesian hierarchical stock-recruitment model. Across most populations, productivity declined with smaller spawner body size and sea surface temperatures that were colder in the winter and warmer in the summer during the first year at sea. Decreased productivity was also associated with above average fall maximum daily streamflow, increased sea ice cover prior to juvenile outmigration, and abundance of marine competitors, but the strength of these effects varied among populations. Maximum daily stream temperature during spawning migration had a nonlinear relationship with productivity, with reduced productivity in years when temperatures exceeded thresholds in main stem rivers. These results demonstrate for the first time that well-documented declines in body size of YK Chinook salmon were associated with declining population productivity, while taking climate into account.

The influence of moisture on precipitation patterns across the Western Tibetan Plateau and its response to sea surface temperature warming

Abstract The distribution of water resources in sub-basins across the Western Tibetan Plateau (WTP) is of critical importance due to not only ecological vulnerability resulting from the extremely arid climatology but also the political sensitivities surrounding the international rivers. In this study, we utilize an advanced water vapor tracer (WVT) embedded in the widely used regional climate model – Weather and Research Forecast (WRF), to quantify moisture contributions from four main sources towards precipitation over the WTP region. We also analyze influences on other sub-basins in the TP for comparison purposes. We examine how changes in sea surface temperature (SST) during 2010s compared to 1980s have influenced precipitation patterns and moisture contributions over recent decades. Our findings indicate that terrestrial moisture sources contribute more than oceanic sources towards the endorheic TP region. Recycling processes originating from highlands area are revealed to play a greater role in contributing moisture over WTP compared to those from lowlands areas. Furthermore, our results demonstrate stronger agreements between wetting distribution patterns and distributions of liquid/solid hydrometeors rather than water vapor distribution itself, highlighting condensation/freezing as critical factors. Notably, we observe different responses within Amu Dayra basin compared to the main WTP when subjected to SST changes. This study focuses on delineating distinct roles of terrestrial and oceanic moisture sources in driving precipitation changes over WTP, while specifically emphasizing condensation process’ contribution to inner TP’s precipitation and highlighting moisture transport form oceans’ influence on precipitation patterns over Amu Dayra basin.

DINEOF Interpolation of Global Ocean Color Data: Error Analysis and Masking

Abstract The Data Interpolation Empirical Orthogonal Function (DINEOF) algorithm is used to reconstruct datasets of geophysical and biological variables such as sea surface temperature (SST) and chlorophyll a (Chl a). In this study, we analyze the impact of both the quantity and distribution of missing data on the performance of DINEOF demonstrating how DINEOF plus a connectivity mask can be used for future data reconstruction tasks. We propose an enhanced version of DINEOF (DINEOF+) by adding two steps: 1) Using a 75% threshold of missing data for reconstructing incomplete datasets and 2) masking interpolated points that lack sufficient space–time observations in the original dataset. We successfully apply DINEOF+ to the Ocean Color Climate Change Initiative (OC-CCI) global daily Chl a dataset and validate the results using in situ datasets. We find that the recovery rate varies across ocean basins and years. In oligotrophic waters, the daily data coverage increased by 40%–50% during the period from 2003 to 2020. Using DINEOF+ allows us to obtain a significantly higher temporal resolution of global Chl a data, which will improve understanding of marine phytoplankton dynamics in response to changing environments. Significance Statement We perform an error analysis on the application of DINEOF for reconstructing a global Chl a dataset. The results of this analysis illustrate the impact of missing data—both in terms of quantity and distribution—on the performance of DINEOF. We propose using DINEOF+, an enhanced version of DINEOF that adds an editing step to mask out interpolated points based on the number of surrounding observations in the original input. The performance of DINEOF+ was validated using both simulated and in situ datasets. The results indicate that employing this masking technique effectively reduces biased estimates of missing data. DINEOF+ can be applied to other biogeochemical variables. However, caution is advised when dealing with observations characterized by high variance.

Detection of Dominant Rainfall Patterns in Indonesian Regions Using Empirical Orthogonal Function (EOF) and Its Relation with ENSO and IOD Events

Several studies indicate that Indonesia’s position on the equator, where winds from the northern and southern hemispheres converge, leads to year-round rainfall in Indonesia. Previous studies have also shown the presence of monsoon winds over Indonesia, which causes rain in Indonesia to follow the Asian and Australian monsoon wind patterns. Recently, variations in rainfall patterns and intensity in Indonesia have been highly volatile and difficult to predict. This study uses gridded data analysis of ocean-atmospheric rainfall for 69 years (1948–2016) to detect monsoonal, equatorial, and local signals and relate them to ENSO and IOD events. Rainfall analysis reveals that the first mode accounts for 38.57%, the second 13.92%, and the third 8.93% of the total variance. These results show that these variants are very variable, both in space and time. Monsoonal patterns were detected in the southern region of Indonesia, equatorial patterns in northern Sumatra and western Kalimantan, while local patterns were detected in Maluku and the Maluku Islands. Furthermore, the monsoonal and local rainfall patterns are strongly correlated (> 0.85) with local SST, but the equatorial rainfall pattern is weakly correlated (< 0.5) with local SST. Note that the equatorial pattern is associated with the Inter Tropical Convergence Zone (ITCZ) phase. The varied vector correlation map illustrates the asymmetric fluctuations in sea surface temperature and the simultaneous existence of winds coming from the west from the Indian Ocean and winds coming from the east from the Pacific Ocean during the period of heavy precipitation.

Impact of Summer North Atlantic Sea Surface Temperature Tripole on Precipitation over Mid–High-Latitude Eurasia

Abstract Eurasia is a sensitive and high-risk region for global climate changes, where climate anomalies significantly influence natural ecosystems, human health, and economic development. The North Atlantic tripole (NAT) sea surface temperature anomaly is crucial to interannual precipitation variations in Eurasia. Several studies have focused on the link between the NAT and climate anomalies in winter and spring. However, the mechanism by which the summer NAT impacts climate anomalies in Eurasia remains unclear. This study examines how the NAT impacts interannual variations of summer precipitation in mid–high-latitude Eurasia. Precipitation variations are associated with the atmospheric teleconnection triggered by the NAT. When the NAT is in its positive phase, the anomalous atmospheric diabatic heating over the North Atlantic excites an equivalent-barotropic Rossby wave train response that propagates eastward toward Eurasia, resulting in atmospheric circulation anomalies over the region. The combined effects of atmospheric circulation, radiative forcing, and water vapor transport anomalies lead to decreased precipitation across northern Europe and central Eurasia, with higher precipitation anomalies over northeast Asia. Finally, numerical experiments verify that the summer NAT excites atmospheric teleconnections that propagate downstream, affecting precipitation anomalies in mid–high-latitude Eurasia. This study provides a scientific basis for predicting Eurasian summer precipitation and strengthening disaster management strategies.