Seasonal Forecasts Research Articles

Using Calibrated Rainfall Forecasts and Observed Rainfall to Produce Probabilistic Meteorological Drought Forecasts

Most existing drought forecast systems rely only on observed or forecast rainfall, losing valuable context gained from considering both. The lack of a direct link between observed and forecast rainfall reduces the physical consistency of a system, motivating the development of a methodology that can directly link the two. The methodology developed in this study allows the comparison of the calibrated ensemble forecasts of rainfall totals from a dynamical climate model to observed rainfall deficiencies from a gridded rainfall analysis. The methodology is used to create a probabilistic product that forecasts the chance of entering meteorological drought, with lead times of one month (monthly forecast) and three months (seasonal forecast). Existing deficiency areas are included to facilitate analysis of how these areas are forecast to change. The performance of the developed methodology was verified using Percent Correct (PC), Brier Score (BS), and Relative Operating Characteristic (ROC) statistics. Analysis of the forecast plots was also completed visually. Forecast performance for areas with existing deficiencies as well as for non-deficiency areas was promising (PC rates of >79% and >97%, respectively). Although PC rates for observed deficiencies were low across most months, the mean forecast probability for these areas was 36%, indicating the system had value and outperformed climatology. A calibrated, coupled product like the one scoped in this study has not been explored and we note that it could be an invaluable tool for quantifying meteorological drought onset and persistence in Australia.

Forecasting Tropical Annual Maximum Wet‐Bulb Temperatures Months in Advance From the Current State of ENSO

AbstractHumid heatwaves, characterized by high temperature and humidity combinations, challenge tropical societies. Extreme wet‐bulb temperatures (TW) over tropical land are coupled to the warmest sea surface temperatures by atmospheric convection and wave dynamics. Here, we harness this coupling for seasonal forecasts of the annual maximum of daily maximum TW (TWmax). We develop a multiple linear regression model that explains 80% of variance in tropical mean TWmax and significant regional TWmax variances. The model considers warming trends and El Niño and Southern Oscillation indices. Looking ahead, the strong‐to‐very‐strong El Niño at the end of 2023, with an Oceanic Niño Index of ∼2.0, suggests a 2024 tropical land mean TWmax of 26.2°C (25.9–26.4°C), and a 68% chance (24%–94%) of breaking existing records. This method also predicts regional TWmax in specific areas.

Smallholder farmers’ perceived motivations for the adoption and implementation of climate information services in the Atwima Nwabiagya District, Ghana

This study investigated smallholder farmers’ perceived motivations for the adoption and implementation of climate information services (CIS) in the Atwima Nwabiagya District, Ghana. Specifically, the study answered the following research questions. (i) What is the perception of smallholder farmers on climate change and variability in the Atwima Nwabiagya District?; (ii) What are the CIS accessed and used by the smallholder farmers for farm management decisions?; (iii) What are the perceived motivations for the adoption and implementation of CIS by smallholder farmers?; and; (iv) What are the barriers confronting smallholder farmers in adopting and implementing CIS? Primary data was collected through questionnaire surveys with 200 farming households and four key informant interviews in four selected communities (Kobeng, Seidi, Wioso, and Nkawie Panin) in the study district. Results showed that smallholder farmers perceived climate change and variability through increased windstorms and thunderstorms, variable rainfall, and increased temperature patterns. The surveyed farmers also perceived that the climatic changes affect their agricultural activities adversely. However, the smallholder farmers accessed and used CIS including weather warnings (86%), daily weather forecasts (70%), and seasonal forecasts (60%) to manage climatic risks on the farm. Results also showed that the smallholder farmers were motivated by critical factors including improvement in the acquisition of proactive measures (92%), increased knowledge of climatic patterns that affect food stock (91%), and a reduction in loss of stock produced (88%) to access and use CIS. Despite this, the smallholder farmers were confronted with key barriers in their uptake and utilization of CIS. These were the timeliness of CIS (73%), a lack of weather symbols in the forecasts (65%), and inadequate information on seasonal forecasts for long-term planning (61%). The study concluded that the motivations for smallholder farmers using CIS in the study district may vary depending on their context, goals, and challenges. However, the general motivation is to enhance smallholder farmers’ farm planning and risk management by using weather and climate forecasts to make better decisions on crop selection, planting dates, input use, pest and disease control, irrigation, harvesting, and marketing.

FSNet: A Hybrid Model for Seasonal Forecasting

FSNet: A Hybrid Model for Seasonal Forecasting

Unveiling Water Allocation Dynamics: A Text Analysis of 25 Years of Stakeholder Meetings

Abstract Managing water resources in regions with high climate variability and frequent extreme weather events poses challenges for policymakers. To facilitate water allocation in these cases, participatory and collaborative decision-making approaches have become common. However, the evaluation of these approaches is hindered by the lack of structured methods and data to understand them. To address this knowledge gap, we propose a novel methodology that leverages text data to identify key topics, conflicts, and influential actors that shape water allocation dynamics. Our methodology is tested using records of 1020 water basin committee meetings held between 1997 and 2021 across twelve basin committees in Ceará, Brazil – a region known for its extensive history of droughts that have impacted water governance. To uncover key water management issues discussed during these meetings, we employed a three-step topic modeling framework: (1) sentence embedding, (2) dimensionality reduction, and (3) sentence clustering. Furthermore, we used entity recognition, dependency parsing, and network graphs to identify powerful actors influencing these meetings and, ultimately, the decisions taken. Our findings revealed stakeholders’ heightened concern for urban water supply over agricultural demand during droughts. We found that “reservoir operation” was the most recurring topic, especially in basins where the strategic reservoirs are located. Discussions related to “climate information” became significantly more important over time, which indicates that water allocation decisions are increasingly based on the seasonal forecast and data on oceanic indices provided by the meteorology agency. Despite the presence of local users in the committees, governmental representatives dominated the discussions and were central in all river basins. In conclusion, our proposed approach harnesses existing text data to uncover spatiotemporal patterns related to participatory water allocation. This study opens new avenues for investigating water governance using text-based analysis.

Winter precipitation predictability in Central Southwest Asia and its representation in seasonal forecast systems

In Central Southwest Asia (CSWA; 22°N to 40°N and 30°E to 70°E), winter (November to February) precipitation contributes up to 70% of the annual mean, but substantial interannual variations exist. Dynamical models exhibit subpar predictability in this region, but the limits of their skills are not well established. Here, we identify the tropical and extratropical forcings that explain ~75% of area-averaged seasonal variability in CSWA winter precipitation. Tropical forcing comes from the indirect El Niño-Southern Oscillation (ENSO) pathway, the leading mode of tropical Indian Ocean precipitation variability. This mode is coupled with ENSO-related Pacific Ocean sea surface temperature variability. A direct ENSO influence on CSWA does not extend beyond its Indian Ocean connection. Extratropical forcing comes from a large-scale mode of internal atmospheric variability. The spatial structure, variability of tropical forcing, and teleconnection with CSWA winter precipitation are skillfully depicted in two seasonal forecasting systems: the fifth-generation seasonal forecasting system (SEAS5) and Seamless System for Prediction and Earth System Research (SPEAR). Extratropical forcing’s spatial structure is also produced skillfully in the two modelling systems; however, the representation of its interannual variability and teleconnection requires improvement. While SEAS5 displays skill in representing extratropical forcing influence on CSWA winter precipitation and marginal skill in reproducing interannual variability, SPEAR has negligible ability in both areas. Consequently, these models have limited predictive skills over CSWA in winter. While improvements in representing extratropical forcing may be inherently limited as it arises from internal atmospheric variability, further research is needed to establish its predictability limits fully.

Applicability of seasonal forecasts from dynamical models for reservoir management practices

Despite the availability of reliable seasonal forecasts of Indian Summer Monsoon Rainfall (ISMR), the use of dynamical models driven by these forecasts for reservoir level management is limited. Reservoir water management can specially be useful if it can be done several months in advance, in view of an impending drought/flood scenario. The applicability of seasonal forecasts from the Monsoon Mission (MM) seasonal forecast model for seasonal and monthly inflow forecasts for tropical Indian reservoirs (Mula and Kangsabati) is studied using the Soil and Water Assessment Tool (SWAT) hydrological model, at a lead time of 3 months. Long-term observed inflow datasets are used for calibration and validation of SWAT-Calibration and Uncertainty Procedure (CUP) with Sequential Uncertainty Fitting (SUFI)-2 algorithm using insitumeteorological data. Observed inflows and inflow simulations are compared with simulated inflow using SWAT with same calibrated parameters, but with forcing derived from reforecasts from the MM model. The SWAT-CUP calibrated well with reasonable Nash Sutcliffe Efficiency (NSE) (Mula = 0.75, Kangsabati = 0.79) and Percentage Bias (PBIAS) (Mula = -28%, Kangsabati = 17%) for both reservoirs. The skill scores for streamflow predictions vary from 0.6-0.70 during the monsoon season, indicating reasonable accuracy for these predictions. The SWAT-MM model has a reasonable skill with 0.52-0.53 NSE and 26%-40% PBIAS. Therefore, SWAT-MM-based model has a good potential to forecast monthly and seasonal reservoir inflow for various agro-climatic zones of India. These forecasts when used in real-time, can serve as a guideline for managing the reservoir storage and release, and hence proving to be of great socio-economic importance.

Regionalization of the Onset and Offset of the Rainy Season in Senegal Using Kohonen Self-Organizing Maps

This study explores the spatiotemporal variability of the onset, end, and duration of the rainy season in Senegal. These phenological parameters, crucial for agricultural planning in West Africa, exhibit high interannual and spatial variability linked to precipitation. The objective is to detect and spatially classify these indices across Senegal using different approaches. Daily precipitation data and ERA5 reanalyses from 1981 to 2018 were utilized. The employed method enables the detection of key dates. Subsequently, the Kohonen algorithm spatially classifies these indices on topological maps. The results indicate a meridional gradient of the onset, progressively later from the southeast to the northwest, whereas the end follows a north–south gradient. The duration varies from 45 days in the north to 150 days in the south. The use of self-organizing maps allows for classifying the onset, end, and duration of the season into four zones for the onset and end, and three zones for the duration of the season. They highlight the interannual irregularity of transitions, with both early and late years. The dynamic analysis underscores the complex influence of atmospheric circulation fields, notably emphasizing the importance of low-level monsoon flux. These findings have tangible implications for improving seasonal forecasts and agricultural activity planning in Senegal. They provide information on the onset, end, and duration classes for each specific zone, which can be valuable for planning crops adapted to each region.

Evaluating soil moisture simulations from a national-scale gridded hydrological model over Great Britain

Study RegionThe study covers sites across Great Britain. Study FocusSoil moisture information is important for a range of applications including flood and drought monitoring, seasonal hydrological forecasting, and agricultural management. However, spatially distributed soil moisture estimates for sub-surface soils are scarce despite their importance. The Grid-to-Grid hydrological model (G2G) was primarily developed to simulate river flows at a national scale, but can also output simulated depth-integrated soil moisture on a 1 km grid. Here, we evaluate G2G soil moisture simulations against in situ neutron probe (NP) observations at 85 sites across Great Britain, to evaluate whether modelled soil moisture outputs have value and to identify areas for improvement. New Hydrological Insights for the Region:Despite large uncertainties in observed soil moisture within a site, there was a good temporal correlation between observed and modelled soil moisture, with Pearson correlation values exceeding 0.7 for 77% of sites. However, the model tended to under-predict soil moisture values (median bias of −12 cm/m) and over-predict variation (median standard deviation error of 2 cm/m). Model agreement with observations was generally better for areas with deep or mid-depth mineral soils and worst in areas of peat. Based on this evaluation against NP observations, we demonstrate that G2G soil moisture is a useful resource for estimating relative wetness of the soil, but not necessarily the soil moisture content values themselves.

Application of advanced very high-resolution radiometer (AVHRR)-based vegetation health indices for modelling and predicting malaria in Northern Benin, West Africa

BackgroundVegetation health (VH) is a powerful characteristic for forecasting malaria incidence in regions where the disease is prevalent. This study aims to determine how vegetation health affects the prevalence of malaria and create seasonal weather forecasts using NOAA/AVHRR environmental satellite data that can be substituted for malaria epidemic forecasts.MethodsWeekly advanced very high-resolution radiometer (AVHRR) data were retrieved from the NOAA satellite website from 2009 to 2021. The monthly number of malaria cases was collected from the Ministry of Health of Benin from 2009 to 2021 and matched with AVHRR data. Pearson correlation was calculated to investigate the impact of vegetation health on malaria transmission. Ordinary least squares (OLS), support vector machine (SVM) and principal component regression (PCR) were applied to forecast the monthly number of cases of malaria in Northern Benin. A random sample of proposed models was used to assess accuracy and bias.ResultsEstimates place the annual percentage rise in malaria cases at 9.07% over 2009–2021 period. Moisture (VCI) for weeks 19–21 predicts 75% of the number of malaria cases in the month of the start of high mosquito activities. Soil temperature (TCI) and vegetation health index (VHI) predicted one month earlier than the start of mosquito activities through transmission, 78% of monthly malaria incidence.ConclusionsSVM model D is more effective than OLS model A in the prediction of malaria incidence in Northern Benin. These models are a very useful tool for stakeholders looking to lessen the impact of malaria in Benin.

A Bagging Ensemble Algorithm for Seasonal Time Series Forecasting

A Bagging Ensemble Algorithm for Seasonal Time Series Forecasting

A hot Spring for Mexico in 2024

Hot days observed during February 2024, reaching 30oC in Mexico City, have made its inhabitants forget that it was the winter season, characterized by dry and cool weather. While such extreme events are often associated with climate change, the current state of the coupled ocean-atmosphere system indicates the last months of an El Niño state, after an expectation of this to be within the top 5 El Niño – Southern Oscillation (ENSO) events (https://cutt.ly/rw1h4T26). The effects of ENSO on the seasonal climate have been widely studied (Philander 1990), and their impacts on Mexico have also been documented (Magana, 2004). Recent seasonal forecasts suggest a progression towards a neutral state for the April-June 2024 season. And further projections indicate a 55% of chance of La Niña developing during the rainy season of 2024. This suggests an end to dry and warm conditions and the possibility of increased rainfall. (Magaña et al. 2003). However, with an ongoing drought over most of the Mexican region, and a water crisis affecting central Mexico, it seems that the seasonal forecasts based on ENSO projections set up the stage for a critical situation for most of the country during the Spring season.

Robustness of hydrometeorological extremes in surrogated seasonal forecasts

AbstractWater and disaster risk management require accurate information about hydrometeorological extremes. However, estimation of rare events using extreme value analysis is hampered by short observational records, with large resulting uncertainties. Here, we present a surrogate world setup that makes use of data samples from meteorological and hydrological seasonal re‐forecasts to explore extremes for long return periods. The surrogate timeseries allow us to pool the re‐forecasts into 1000‐year‐long timeseries. We can then calculate return values of extremes and explore how they are affected by the size of sub‐samples as method for estimating the uncertainty. The approach relies on the fact that probabilistic seasonal re‐forecasts, initialized with perturbed initial conditions, have limited predictive skill with increasing lead time. At long lead times re‐forecasts will diverge into independent samples. The meteorological seasonal re‐forecasts are taken from the SEAS5 system, and hydrological re‐forecasts are generated with the E‐HYPE process‐based model for the pan‐European domain. Extreme value analysis is applied to annual maxima of precipitation and streamflow for return periods of 100 years. The analysis clearly demonstrates the large uncertainty in long return period estimates with typical available samples of only few decades. The uncertainty is somewhat reduced for 100‐year samples, but several 100 years seem to be necessary to have robust estimates. The bootstrap with replacement approach is applied to shorter timeseries, and is shown to well reproduce the uncertainty range of the longer samples. However, the main estimate of the return value can be significantly offset. Although the method is model based, with the associated uncertainties and bias compared to the real world, the surrogate approach is likely useful to explore rare and compounding extremes.

The potential value of seasonal drought forecasts in the context of climate change: A case study of the African elephant conservation sector

AbstractThis study investigates meteorological drought in sub‐Saharan Africa within the context of elephant conservation. Prolonged drought significantly impacts elephants, leading to increased mortality rates and heightened human–elephant conflicts. We assess both the anticipated 21st century changes in impact‐relevant meteorological drought metrics and the efficacy of existing forecasting systems in predicting such droughts on seasonal time scales. The climate change element of our study uses the 6th Coupled Model Intercomparison Project (CMIP6) ensemble to evaluate projected change in 3‐month Standardized Precipitation Index (SPI3). We then carry out a quantitative assessment of seasonal forecast skill, utilizing 110 years of precipitation hindcasts generated by the European Centre for Medium Range Forecasting (ECMWF) system. Our findings indicate that persistent drought is projected to become more frequent over the 21st century in southern Africa, where the majority of elephants reside. Analysis of seasonal hindcasts indicates that, while the forecasts have greater skill than climatology, they remain highly uncertain. Previous work suggests that it may be possible to reduce this uncertainty by contextualizing forecasts within specific climate regimes. However, even with improved forecast skill, effective action hinges on the alignment of forecasts with the practical needs of conservation practitioners. Over the next decades, a co‐production approach will be critical for leveraging seasonal forecasts for climate change adaptation within the conservation sector.

Seasonal forecasting of local-scale soil moisture droughts with Global BROOK90: a case study of the European drought of 2018

Abstract. Prolonged deficit of soil moisture can result in significant ecosystem and economical losses. General slowdown of vegetation growth and development, withering of foliage cover, reduction of carbon, nutrients and water cycling, increase of fire and insect outbreaks are just a few examples of soil moisture drought impacts. Thus, an early and timely warning via monitoring and forecast could help to prepare for a drought and manage its consequences. In this study, a new version of Global BROOK90, an automated framework to simulate water balance at any location, is presented. The new framework integrates seasonal meteorological forecasts (SEAS5 forecasting system) from European Centre for Medium-Range Weather Forecasts (ECMWF). Here we studied how well the framework can predict the soil moisture drought on a local scale. Twelve small European catchments (from 7 to 115 km2) characterized by various geographical conditions were chosen to reconstruct the 2018–2019 period, when a large-scale prolonged drought was observed in Europe. Setting the ERA5-forced soil moisture simulations as a reference, we analysed how the lead time of the SEAS5 hindcasts influences the quality of the soil moisture predictions under drought and non-drought conditions. It was found that the hindcasted soil moisture fits well with the reference model runs only within the first (in some cases until the second and third) month of lead time. Afterwards, significant deviations up to 50 % of soil water volume were found. Furthermore, within the drought period the SEAS5 hindcast forcings resulted in overestimation of the soil moisture for most of the catchment, indicating an earlier end of a drought period. Finally, it was shown that application of the probabilistic forecast using the ensembles' quantiles to account for the uncertainty of the meteorological input is reasonable only for a lead time of up to 3 months.

Evaluating Streamflow Forecasts in Hydro‐Dominated Power Systems—When and Why They Matter

AbstractThe value of seasonal streamflow forecasts for the hydropower industry has long been assessed by considering metrics related to hydropower availability. However, this approach overlooks the role played by hydropower dams within the power grid, therefore providing a myopic view of how forecasts could improve the operations of large‐scale power systems. With the aim of understanding how the value of streamflow forecasts penetrates through the power grid, we developed a coupled‐water energy model that is subject to reservoir inflow forecasts with different levels of predictive performance. We implement the modeling framework on a real‐world case study based on the Cambodian grid, which relies on hydropower, coal, oil, and imports from neighboring countries. In particular, we evaluate the performance in terms of metrics selected from both the reservoir and power systems, including available and dispatched hydropower, power production costs, CO2 emissions, and transmission line congestion. Through this framework, we demonstrate that streamflow forecasts can positively impact the operations of hydro‐dominated power systems, especially during the transition from wet to dry seasons. Moreover, we show that the value largely varies with the specific metric of performance at hand as well as the level of operational integration between water and power systems.

Probabilistic seasonal precipitation forecasts using quantiles of ensemble forecasts

Seasonal precipitation forecasting is vital for weather-sensitive sectors. Global Circulation Models (GCM) routinely produce ensemble Seasonal Climate Forecasts (SCFs) but suffer from issues like low forecast resolution and skills. To address these issues in this study, we introduce a post-processing method, Quantile Ensemble Bayesian Model Averaging (QEBMA). It utilises quantiles from a GCM ensemble forecast to create a pseudo-ensemble forecast. Through their reasonable linear relationships with observations, each pseudo-member connects a hurdle distribution with a point mass at zero for dry months and a gamma distribution for wet months. These distributions are mixed to construct a forecast probability distribution with their weights, proportional to the quantiles’ historical forecast performance. QEBMA is applied to three GCMs, including GloSea5 from the United Kingdom, ECMWF from Europe and ACCESS-S1 from Australia, for monthly precipitation forecasts in 32 locations across four climate zones in Australia. Leave-one-month-out cross-validation results illustrate that QEBMA enhances forecast skills compared to raw GCMs and other post-processing techniques, including quantile mapping and Extended Copula Post-Processing (ECPP), for forecast lead time of 0 to 2 months, based on five metrics. The skill improvements achieved by QEBMA are often statistically significant, particularly when compared to raw GCM forecasts across the 32 study locations. Among these post-processing models, only QEBMA consistently outperforms the SCF benchmark climatology, offering a promising alternative for improving seasonal precipitation forecasts.

Incorporating indigenous knowledge systems-based climate services in anticipatory action in Zimbabwe: an ex-ante assessment

IntroductionThe success of anticipatory action (AA) in climate change related disaster risk reduction depends largely on the ability to accurately predict adverse weather events and to subsequently take appropriate and timely action. The current seasonal climate forecasts obtained through national and global forecasting centers such as the Zimbabwe Meteorological Services Department (MSD) and the European Center for Medium-Range Weather Forecasts (ECMWF) have been found to have several limitations including limited skill in intra-seasonal variability which is vital in most processes, lack of accessibility, difficult interpretability and applicability to local communities. As a result, calls for the integration of Indigenous Knowledge Systems (IKS)-based climate services in seasonal climate forecasting to bolster the efficacy and accuracy of forecasts are increasing.MethodologyThe study employed a mixed methods approach combining a survey of 200 household heads, document reviews and key informant interviews with IKS specialists, AA community of practice members and disaster risk reduction specialists.ResultsThis article presents the findings of a study carried out in four semi-arid districts of Zimbabwe (Mbire, Matobo, Binga, and Mudzi) to assess the feasibility of integrating IKS into the drought AA mechanism at national, district, and ward level. We find that 82% of the surveyed households used indigenous knowledge systems for seasonal climate forecasting and extreme weather forecasting combined with scientific forecasts. Respondents demonstrated preference for triangulated forecasting to increase reliability. Both the meteorological approaches to seasonal climate forecasting and the IKS based forecasting had different strengths and weaknesses.DiscussionBased on the found gaps in current seasonal forecasting techniques, local applicability, and limited quantitative analysis in IKS, this study provides a guideline on how scientific and IKS forecasting can be triangulated for leveraging forecasting information in Zimbabwe. The use of IKS may be of utmost relevance to agencies and entities seeking the achievement of drought risk reduction through AA programmes.

Predictability of marine heatwaves: assessment based on the ECMWF seasonal forecast system

Abstract. Marine heatwaves (MHWs), defined as prolonged period of extremely warm sea surface temperature (SST), have been receiving a lot of attention in the past decade as their frequency and intensity increase in a warming climate. This paper investigates the extent to which the seasonal occurrence and duration of MHWs can be predicted with the European Centre for Medium-Range Weather Forecast (ECMWF) operational seasonal forecast system. The prediction of the occurrence of MHW events, the number of MHW days per season, and their intensity and spatial extent are derived from seasonal SST forecasts and evaluated against an observation-based SST analysis using both deterministic and probabilistic metrics over the 1982–2021 period. Forecast scores show useful skill in predicting the occurrence of MHWs globally for the two seasons following the starting date. The skill is the highest in the El Niño region, the Caribbean, the wider tropics, the north-eastern extra-tropical Pacific, and southwest of the extra-tropical basins. The skill is not as good for other midlatitude eastern basins nor for the Mediterranean, with the forecast system being able to represent the low-frequency modulation of MHWs but showing poor skill in predicting the interannual variability of the MHW characteristics. Linear trend analysis shows an increase in MHW occurrence at a global scale, which the forecasts capture well.

Connection of Summer Surface Temperature Anomalies between the East European Plain–West Siberian Plain and North America on the Interannual Time Scale

Abstract This study identifies a significantly positive relationship between summer surface air temperature (SAT) anomalies over two remote regions in the Eurasian continent and North America during the period 1979–2021 on the interannual time scale. The former region includes the East European Plain and the West Siberian Plain, and the latter region includes central and eastern North America. The regionally averaged summer SAT anomalies show a correlation coefficient of 0.66 between these two regions, which is significant at the 99% confidence level. This intercontinental SAT relationship can be explained by a wavelike pattern of circulation anomalies, which is the leading mode of upper-tropospheric circulation anomalies over the middle and high latitudes of the Northern Hemisphere in summer. Further analysis suggests that the sea surface temperature (SST) anomalies over the Pacific and North Atlantic in the preceding spring, being coupled with the leading mode of atmospheric circulation anomalies over the Pacific–Atlantic sector, persist into summer and affect the SATs in the two remote regions, resulting in the intercontinental SAT connection. Significance Statement Summer surface air temperature (SAT) has profound effects on public health and agricultural production. Here we find a significantly positive relationship between interannual variations of summer SATs over two remote regions, one in the Eurasian continent and the other in North America. This intercontinental relationship in SATs can be explained as a result of atmosphere–ocean coupling over the Pacific and North Atlantic in the preceding spring. The result is likely to be a critical implication for the seasonal forecast of SAT variations over the two regions. In addition, the concurrence of higher or lower temperatures in these two regions may have impacts on global grain production, since these two regions include many major grain-producing areas in the world.