Seasonal Prediction Research Articles

Improving the Predictability of the US Seasonal Surface Temperature With Convolutional Neural Networks Trained on CESM2 LENS

AbstractTo better understand and improve the prediction of the seasonal surface temperature (TS) across the United States, we employed convolutional neural network (CNN) models trained on the Community Earth System Model Version 2 Large Ensemble (CESM2 LENS). We used lagged sea surface temperatures (SST) over the tropical Pacific region, containing the information of the El Niño Southern Oscillation (ENSO), as input for the CNN models. ENSO is the principal driver of variability in seasonal US surface temperatures (TSUS) and employing CNN models allows for spatiotemporal aspects of ENSO to be analyzed to make seasonal TSUS predictions. For predicting TSUS, the CNN models exhibited significantly improved skill over standard statistical multilinear regression (MLR) models and dynamical forecasts across most regions in the US, for lead times ranging from 1 to 6 months. Furthermore, we employed the CNN models to predict seasonal TSUS during extreme ENSO events. For these events, the CNN models outperformed the MLR models in predicting the effects on seasonal TSUS, suggesting that the CNN models are able to capture the ENSO‐TSUS teleconnection more effectively. Results from a heatmap analysis demonstrate that the CNN models utilize spatial features of ENSO rather than solely the magnitude of the ENSO events, indicating that the improved skill of seasonal TSUS is due to analyzing spatial variation in ENSO events. The proposed CNN model demonstrates a promising improvement in prediction skill compared to existing methods, suggesting a potential path forward for enhancing TSUS forecast skill from subseasonal to seasonal timescales.

Seasonal predictions of summer compound humid heat extremes in the southeastern United States driven by sea surface temperatures

Humid heat extreme (HHE) is a type of compound extreme weather event that poses severe risks to human health. Skillful forecasts of HHE months in advance are crucial for developing strategies to enhance community resilience to extreme events1,2. This study demonstrates that the frequency of summertime HHE in the southeastern United States (SEUS) can be skillfully predicted 0–1 months in advance using the SPEAR (Seamless system for Prediction and EArth system Research) seasonal forecast system. Sea surface temperatures (SSTs) in the tropical North Atlantic (TNA) basin are identified as the primary driver of this prediction skill. The responses of large-scale atmospheric circulation and winds to anomalous warm SSTs in the TNA favor the transport of heat and moisture from the Gulf of Mexico to the SEUS. This research underscores the role of slowly varying sea surface conditions in modifying large-scale environments, thereby contributing to the skillful prediction of HHE in the SEUS. The results of this study have potential applications in the development of early warning systems for HHE.

Influences on North‐Atlantic summer climate from the El Niño‐Southern Oscillation

AbstractSeasonal‐range predictability of summer climate in northwestern Europe is generally considered to be low. This is an increasing issue given the worsening impact of summer heatwaves, droughts and intense convective rainfall in a rapidly changing climate. In wintertime, predictive skill in the region is derived from a variety of sources, not least teleconnections with the El Niño‐Southern Oscillation (ENSO). Summer ENSO teleconnections, however, are often considered to be negligible. In this paper, we revisit the topic of summer teleconnections between ENSO and the North Atlantic‐European region. We build on previous work identifying upper tropospheric responses to tropical forcing, since dynamical teleconnections are most apparent at this level. Our results confirm that significantly increased geopotential heights are found stretching over the North‐Atlantic region and into western Europe when La Niña conditions are prevalent during summer. This pattern is part of the previously identified ‘circumglobal’ pattern of wider northern‐hemisphere height changes. We then look for these responses in a range of climate models used in operational seasonal prediction. While parts of the circumglobal pattern are weakly present, none of them produce the response seen over the North Atlantic, even when the effect of sampling on the observed teleconnection is accounted for. We additionally estimate the contribution of the previous (wintertime) phase of ENSO on the following summer. We find a significant delayed response, particularly in heights, to the earlier phase. The combination of the delayed and current responses gives height anomalies that are larger, on average, when ENSO changes phase from winter to summer. Finally, we show that a modest level of regional prediction skill from ENSO does exist. There is a contribution to skill in heights from the previous ENSO phase, but the equivalent contribution to the skill of zonal winds is smaller.

Large model biases in the Pacific centre of the Northern Annular Mode due to exaggerated variability of the Aleutian Low

AbstractThe Northern Annular Mode (NAM) is traditionally defined as the leading empirical orthogonal function (EOF) of mean sea‐level pressure (MSLP) anomalies during winter. Previous studies have shown that the Pacific centre‐of‐action of the NAM is typically more amplified in models than in reanalysis. Here, we analyse the NAM in hindcasts from nine seasonal prediction models over 1993/1994–2016/2017. In all the models, the Pacific centre‐of‐action is much larger than in reanalysis over that period, during which the NAM and the North Atlantic Oscillation (NAO) are almost indistinguishable. As a result, the NAM in the models is correlated with Aleutian Low variability around four times more strongly than in reanalysis. We show that this discrepancy can be explained primarily by the amplitude of Aleutian Low variability, which is on average 17% higher in models than in reanalysis, with a secondary effect from a stronger correlation between the Aleutian Low and NAO. When the NAM is computed using zonally averaged MSLP, the Aleutian Low amplitude does not influence the pattern directly. Instead, the amplitude of the Pacific centre‐of‐action is governed primarily by the correlation between the Aleutian Low and NAO, reducing the apparent Pacific biases in models. While the two methods yield almost identical results in reanalysis, the large Aleutian Low biases result in differences when applied to model data. Modifying the MSLP statistically to alter the Aleutian Low amplitude reveals that the spatial pattern of the traditionally defined NAM is highly sensitive to Aleutian Low variability, even without modifying the correlation between the Aleutian Low and NAO. Hence, the NAM in models may not be as biased as the traditional method would suggest. We therefore conclude that the traditional EOF method is unsuitable for defining the NAM in the presence of highly amplified Aleutian Low variability, and encourage the use of the zonal‐mean method.

The Influence of ENSO Diversity on Future Atlantic Tropical Cyclone Activity

Abstract El Niño–Southern Oscillation (ENSO) influences seasonal Atlantic tropical cyclone (TC) activity by impacting environmental conditions important for TC genesis. However, the influence of future climate change on the teleconnection between ENSO and Atlantic TCs is uncertain, as climate change is expected to impact both ENSO and the mean climate state. We used the Weather Research and Forecasting Model on a tropical channel domain to simulate 5-member ensembles of Atlantic TC seasons in historical and future climates under different ENSO conditions. Experiments were forced with idealized sea surface temperature configurations based on the Community Earth System Model (CESM) Large Ensemble representing: a monthly varying climatology, eastern Pacific El Niño, central Pacific El Niño, and La Niña. The historical simulations produced fewer Atlantic TCs during eastern Pacific El Niño compared to central Pacific El Niño, consistent with observations and other modeling studies. For each ENSO state, the future simulations produced a similar teleconnection with Atlantic TCs as in the historical simulations. Specifically, La Niña continues to enhance Atlantic TC activity, and El Niño continues to suppress Atlantic TCs, with greater suppression during eastern Pacific El Niño compared to central Pacific El Niño. In addition, we found a decrease in the Atlantic TC frequency in the future relative to historical regardless of ENSO state, which was associated with a future increase in northern tropical Atlantic vertical wind shear and a future decrease in the zonal tropical Pacific sea surface temperature (SST) gradient, corresponding to a more El Niño–like mean climate state. Our results indicate that ENSO will remain useful for seasonal Atlantic TC prediction in the future.

Improving Subseasonal Soil Moisture and Evaporative Stress Index Forecasts through Machine Learning: The Role of Initial Land State versus Dynamical Model Output

Abstract The effect of machine learning and other enhancements on statistical–dynamical forecasts of soil moisture (0–10 and 0–100 cm) and a reference evapotranspiration fraction [evaporative stress index (ESI)] on subseasonal time scales (15–28 days) are explored. The predictors include the current and past land surface conditions and dynamical model hindcasts from the Subseasonal to Seasonal Prediction project (S2S). When the methods are enhanced with machine learning and other improvements, the increases in skill are almost exclusively coming from predictors drawn from observations of current and past land surface states. This suggests that operational S2S flash drought forecasts should focus on optimizing use of information on current conditions rather than on integrating dynamically based forecasts, given the current state of knowledge. Nonlinear machine learning methods lead to improved skill over linear methods for soil moisture but not for ESI. Improvements for both soil moisture and ESI are realized by increasing the sample size by including surrounding grid points in training and increasing the number of predictors. In addition, all the improvements in the soil moisture forecasts predominantly impact soil moistening rather than soil drying—i.e., prediction of conditions moving away from drought rather than into drought—especially when the initial soil state is drier than normal. The physical reasons for the nonlinear machine learning improvements are also explored. Significance Statement Rapidly intensifying droughts pose extra challenges for predictability. Here, dynamical forecast model output is combined with nonlinear machine learning methods to improve forecasts of rapid changes in soil moisture and the evaporative stress index (ESI).

Application of Long Short-Term Memory (LSTM) Network for seasonal prediction of monthly rainfall across Vietnam

Application of Long Short-Term Memory (LSTM) Network for seasonal prediction of monthly rainfall across Vietnam

Two‐way feedback between the Madden–Julian Oscillation and diurnal warm layers in a coupled ocean–atmosphere model

AbstractDiurnal warm layers develop in the upper ocean on sunny days with low surface wind speeds. They rectify intraseasonal sea‐surface temperatures (SSTs), potentially impacting intraseasonal weather patterns such as the Madden–Julian Oscillation (MJO). Here we analyse 15‐lead‐day forecast composites of coupled ocean–atmosphere and atmosphere‐only numerical weather prediction (NWP) models of the UK Met Office to reveal that the presence of diurnal warming of SST (dSST) leads to a faster MJO propagation in the coupled model compared with the atmosphere‐only model. To test the feedback between the MJO and the dSST, we designed a set of experiments with instantaneous vertical mixing over the top 5 or of the ocean component of the coupled model. Weaker dSST in the mixing experiments leads to a slower MJO over 15 lead days. The dSST produces a increase in the MJO phase speed between the coupled and the atmosphere‐only model. An additional increase is found for other coupling effects, unrelated to the dSST. A two‐way feedback manifests in the coupled model over the 15 lead days of the forecast between the MJO and the dSST. The MJO regime dictates the strength of the dSST and the dSST rectifies the intraseasonal anomalies of SST in the coupled model. Stronger dSST in the coupled model leads to stronger intraseasonal anomalies of SST. The MJO convection responds to these SSTs on a seven‐lead‐day timescale, and feeds back into the SST anomalies within the next three lead days. Overall, this study demonstrates the importance of high vertical resolution in the upper ocean for predicting the eastward propagation of the MJO in an NWP setting, which is potentially impactful for seasonal predictions and climate projections, should this feedback be unrepresented in the models.

Multidecadal Changes of the Seasonal Potential Predictability of Winter PNA and Associated Circulation Anomalies

Abstract Utilizing ensemble hindcast data from the Community Earth System Model (CESM) spanning the years 1900–2014, the multidecadal changes in the seasonal potential predictability of the winter Pacific–North American (PNA) teleconnection pattern and associated circulation anomalies have been investigated by using an information-based metric of relative entropy and the method of the most predictable component analysis. Results show that the seasonal potential predictability of winter PNA has significant multidecadal changes, with values much higher at the two ends of the twentieth century and much lower in between particularly in the 1930s and 1940s. The changes in the seasonal potential predictability of winter PNA are mostly reflected by the temporal evolutions of PNA rather than the location changes of active centers. Further, the changes are mostly contributed by the external forcing of El Niño–Southern Oscillation (ENSO)-related sea surface temperature anomalies in tropical central and eastern Pacific. In particular, the combined effects of lower amplitudes, reduced persistence, and a more eastward shift in warming centers lead to the reduced seasonal potential predictability of PNA and associated circulation changes in the 1930s and 1940s. Significance Statement Seasonal prediction of the winter Pacific–North American (PNA) teleconnection pattern and associated circulation anomalies is very important due to its profound climate impacts. Understanding the multidecadal fluctuations and its driving sources of the potential predictability of winter PNA and associated circulation anomalies are meaningful for skillful seasonal prediction of winter PNA and circulation anomalies as well as related climate variations. This study for the first time shows that the multidecadal fluctuations of the potential predictability of winter PNA are quite significant and the changes are mostly reflected by its temporal evolutions rather than spatial shifts of active centers. Furthermore, this study shows that the strength, persistence, and warming center locations of ENSO-related sea surface temperatures in tropical Pacific play a crucial role on the multidecadal changes of the potential predictability of winter PNA and associated circulation anomalies.

Performance of weather research forecasting model for seasonal prediction of precipitation over Indonesian maritime continent

The precipitation over the Indonesian Maritime Continent (IMC) is one of the most challenging atmospheric parameters to predict accurately. The Weather Research Forecasting (WRF) model used in this study produces overestimated predictions of precipitation intensity compared to satellite data. Therefore, it is necessary to modify the model output to minimize the bias between predictions and satellite observations. The modification includes adjusting the dynamic model parameters and settings to better reflect atmospheric conditions in the IMC region, applying bias correction techniques through a linear scaling method, aligning the monthly average of the model's output with observational data, and conducting statistical analysis in several areas within the IMC. This procedure has significantly reduced the biases and is considered acceptable for each satellite area. Based on the results of the statistical analysis and by applying the precipitation threshold criteria, the accuracy of the predictions in each observation area is quite good, ranging from 0.59 to 1.0. Precipitation with a threshold of 50 mm/day or higher exhibits good accuracy, with a minimum value of 0.59 for RI and a maximum value of 0.97 for RIII and RV. On the other hand, precipitation with a threshold of 20 mm/day or higher demonstrates very good accuracy, with values of 0.97 for RI and 1.00 for RIII to RVI.

Modeling Risk in Fusarium Head Blight and Yield Analysis in Five Winter Wheat Production Regions of Hungary

The five-year mean yield of five Hungarian wheat production counties was 5.59 t ha−1 with a 7.02% average coefficient of variation. There was a regional effect on yield when progressing from south to north with a 1–2 °C higher mean winter air temperature, meaning that the Ta in southern counties increased the five-season mean yield by 15.9% (p = 0.002) compared to the yield of northern counties. Logistic regression models developed to assess the FHB risk driven by a few meteorological variables (Ta; RH) provided proper predictive performance. The results in the regression model were validated against the measured infection rates (P%) provided by the NÉBIH 30 days before and after heading. The FHB pressure was comparatively higher in Zala County, probably due to its special topological and growing conditions, irrespective of the season. Across all areas studied, two of the five identified counties (Pest and Somogy) provided the best classification for FHB infection. In the remaining three counties, the seasonal mean prediction accuracy (differences) exceeded 10% in only 6 out of 30 model outputs. The modeled five-season P% values averaged 70.4% and 93.2% of the measured infection rates for models 1 and 2, respectively. The coincidence of wet and warm weather around the time of wheat flowering enhanced the risk of FHB.

Investigating the seasonal SST Predictability in the Northern Tropical Atlantic Ocean in an ensemble prediction system

The present study comprehensively investigates the practical and intrinsic predictability of the sea surface temperature (SST) in the Northern Tropical Atlantic (NTA) based on the 138-year-long coupled hindcasts with a recently developed seasonal ensemble prediction system. This system can yield skillful deterministic predictions for the prominent warm and cold events at least 6 months ahead. Notably, it excels in providing probabilistic predictions for below- and above-normal events rather than for neutral events. The predictability of SST in the NTA undergoes remarkable seasonal variation with two peaks of predictability targeted at April and October regardless of the lead time. Various sources of predictability for these target months are revealed. For the target month of April, the preceding remote forcing from the El Niño–Southern Oscillation (ENSO) in the tropical Pacific Ocean combined with local signal results in the phase locking of the SST variation and seasonality of signal component over the NTA. This ultimately contributes to the high predictability targeted at April. However, From the perspective of potential predictability of the predictability targeted at October, which has been rarely mentioned in previous studies. It is also encouraging that, similar to the Indian Ocean Dipole, ENSO and the signal-to-noise ratio of the system mainly contribute to predictability beyond persistence at long lead times for the spring SST in the NTA. This indicates that potential future ENSO improvements may leave much room for improvement in the current SST prediction in the NTA.

Suitability of the CICE sea ice model for seasonal prediction and positive impact of CryoSat-2 ice thickness initialization

Abstract. The Los Alamos Community Ice CodE (CICE) sea ice model is being tested in standalone mode to identify biases that limit its suitability for seasonal prediction, where it is driven by atmospheric forcings from the NCEP Climate Forecast System Reanalysis (CFSR) and a built-in mixed-layer ocean model in CICE. The initial conditions for the sea ice and mixed-layer ocean are also from CFSR in the control experiments. The simulated sea ice extent agrees well with observations during the warm season at all lead times up to 12 months, in both the Arctic and the Antarctic. This suggests that CICE is able to provide useful sea ice edge information for seasonal prediction. However, the model's initial conditions have ice that is too thick in the Beaufort Sea, resulting in excessive ice extent in the Arctic at 6-month lead forecasts and errors in ice volume at all lead times when compared to available observations. To address this limitation, additional CS2_IC experiments were conducted, where the Arctic ice thickness was initialized using CryoSat-2 satellite observations while keeping all other initial fields the same as in the control experiments. This reduced the positive bias in the ice thickness in the initial conditions, leading to improvements in both the simulated ice edge and the ice thickness at the seasonal timescale. This indicates that CICE has the potential to improve its seasonal forecast skill and provide more accurate predictions of sea ice extent and thickness when initialized with a more realistic sea ice thickness. This study highlights that the suitability of CICE for seasonal prediction depends on various factors, including initial conditions such as sea ice thickness, in addition to sea ice coverage, as well as oceanic and atmospheric conditions.

Predicting September Arctic Sea Ice: A Multimodel Seasonal Skill Comparison

Abstract This study quantifies the state of the art in the rapidly growing field of seasonal Arctic sea ice prediction. A novel multimodel dataset of retrospective seasonal predictions of September Arctic sea ice is created and analyzed, consisting of community contributions from 17 statistical models and 17 dynamical models. Prediction skill is compared over the period 2001–20 for predictions of pan-Arctic sea ice extent (SIE), regional SIE, and local sea ice concentration (SIC) initialized on 1 June, 1 July, 1 August, and 1 September. This diverse set of statistical and dynamical models can individually predict linearly detrended pan-Arctic SIE anomalies with skill, and a multimodel median prediction has correlation coefficients of 0.79, 0.86, 0.92, and 0.99 at these respective initialization times. Regional SIE predictions have similar skill to pan-Arctic predictions in the Alaskan and Siberian regions, whereas regional skill is lower in the Canadian, Atlantic, and central Arctic sectors. The skill of dynamical and statistical models is generally comparable for pan-Arctic SIE, whereas dynamical models outperform their statistical counterparts for regional and local predictions. The prediction systems are found to provide the most value added relative to basic reference forecasts in the extreme SIE years of 1996, 2007, and 2012. SIE prediction errors do not show clear trends over time, suggesting that there has been minimal change in inherent sea ice predictability over the satellite era. Overall, this study demonstrates that there are bright prospects for skillful operational predictions of September sea ice at least 3 months in advance.

Assessment of meteorological parameters in predicting seasonal temperature of Dhaka city using ANN

In this study, an attempt has been made to investigate the possibility of a machine learning model, Artificial Neural Network (ANN) for seasonal prediction of the temperature of Dhaka city. Prior knowledge of temperature is essential, especially in tropical regions like Dhaka, as it aids in forecasting heatwaves and implementing effective preparedness schemes. While various machine learning models have been employed for the prediction of hot weather across the world, research specially focused on Bangladesh is limited. Additionally, the application of machine learning models needs to be curated to suit the particular weather features of any region. Therefore, this study approaches ANN method for prediction of the temperature of Dhaka exploring the underlying role of related weather variables. Using the daily data for the months of February to July collected from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis data (0.25° × 0.25° global grid) for the years 2011–2020, this study focuses on finding the combination of weather variables in predicting temperatures. The densely populated city, Dhaka, has faced severe consequences due to extreme climate conditions in recent years, and this study will pave a new dimension for further research regarding the topic.

Hybridization Model for Air Pollution Prediction Using Time Series Data

In recent years, data science analysis, particularly time series predictions, has been widely employed across various industrial sectors. However, time series data presents high complexity, especially in seasonal patterns such as monthly, daily, or hourly fluctuations. Irregular fluctuations and external factors increasingly challenge accurate predictions. Therefore, this research proposes a hybrid approach combining SVR-SARIMA, SVR-Prophet, LSTM-SARIMA, and LSTM-Prophet to enhance time series prediction accuracy. This study followed the OSEMN methodology approach: gathering data, cleaning data, exploring data, developing models, and interpreting crucial aspects of problem-solving. Seasonal effect predictions indicated a rise in SO2 and NO2 during dry and rainy seasons until the next two years (average daily increments of 0.0831 μg/m3 for SO2 and 0.0516 μg/m3 for NO2). Estimates suggest a decrease in the order of three particles. The evaluation showed that the SVR model performed better compared to the other three models (RMSE 7.765, MAE 5.477, and MAPE 0.261). The best-performing hybrid model was LSTM-Prophet (99.74% accuracy) with RMSE 12.319, MAE 12.057, and MAPE 0.259 values.

Improving the seasonal forecast by utilizing the observed relationship between the Arctic Oscillation and Northern Hemisphere surface air temperature

Although the seasonal prediction skill of climate models has improved significantly in recent decades, the prediction skill of the Arctic Oscillation (AO), the dominant climate mode over the Northern Hemisphere, remains poor. Additionally, the local representation of AO impacts has diverged from observations, which limits seasonal prediction skill of climate models. In this study, we attempted to improve prediction skill of surface air temperature (SAT) with two post-processing on dynamical model’s seasonal forecast: (1) correction of the AO impact on SAT pattern, and (2) correction of AO index (AOI). The first correction involved replacing the inaccurately simulated impact of AO on SAT with that observed. For the second correction, we employed a empirical prediction model of AOI based on multiple linear regression model based on three precursors: summer sea surface temperature, autumn sea-ice concentration, and autumn snow cover extent. The application of the first correction led to a decrease in prediction skills. However, a significant improvement in SAT prediction skills is achieved when both corrections are applied. The average correlation coefficients for the North America and Eurasian regions increased from 0.23 and 0.06 to 0.28 and 0.30, respectively.

Skillful seasonal prediction of Afro-Asian summer monsoon precipitation with a merged machine learning and large ensemble approach

Afro-Asian summer monsoon precipitation (AfroASMP) is the life blood of billions of people living in many developing countries covering West Africa and Asia. Its complex variabilities are always accompanied by natural disasters like floods, landslides and droughts. Reliable AfroASMP prediction several months in advance is valuable for not only decision-makers but also regional socioeconomic sustainability. To address the current predicament of the AfroASMP seasonal prediction, this study provides an effective machine-learning model (Y-model). Y-model uses the monsoon related big climate data for searching the potential predictors, encompassing atmospheric internal factors and external forcings. Only the predictors associated with significant anomalies in summer horizonal winds at 850 hPa over the monsoon domain are retained. These selected predictors are then reorganized into a large ensemble based upon different thresholds of four fundamental principles. These principles include the focused sample sizes, the relationships between predictors and predictand, the independence among predictors, and the extremities of predictors in the forecast year. Real-time predictions can be generated based on the ensemble mean of skillful members during an independent hindcast period. Y-model skillfully predicts four monsoon precipitation indices of AfroASMP during 2011–2022 at lead 4–12 months, correlation skills range from 0.58 to 0.90 and root mean square error skills are reduced by 11–53% compared to CFS v2 model at lead 1 month. This study offers an effective method for preprocessing predictors in seasonal climate prediction.

Influences of large scale circulation and atmospheric rivers on US winter precipitation beyond ENSO

Abstract This study aims to understand the underlying mechanism of large scale circulation control on atmospheric rivers (AR) and precipitation variability across the Contiguous United States (CONUS) in winter. The El Niño-Southern Oscillation (ENSO), known as a key driver of global circulation, has shown a modest impact on CONUS precipitation, prompting us to focus our attention on other climate drivers. Here, we find that barotropic instability over the exit region of the North Pacific subtropical jet stream plays a critical role in forming a downstream stationary Rossby wave train during winter (referred to as the West Mode). This wave pattern influences CONUS precipitation by affecting AR activity and explains approximately 50% of rainfall changes in the Western US, as well as numerous extreme wet and drought years along the West Coast, such as the wet winter in 2022/23. Over the past eight decades, the West Mode exhibited limited sensitivity to both Sea Surface Temperature (SST) and increasing anthropogenic forcing and was more influential in shaping interannual and interdecadal CONUS precipitation variability than ENSO. This result may explain why ENSO alone can only account for a limited portion of CONUS precipitation variability, thereby imposing an inherent constraint on the precision of seasonal predictions of CONUS precipitation made by climate models. Due to the significance of the West Mode in governing precipitation variability over the Western US, winter precipitation in that region may possess some resilience to the effects of global warming in the coming decades, as supported by large ensemble simulations driven by projected radiative forcing.

Seasonal prediction of midsummer compound heat-humidity events over Southeast China

In recent years, there has been an increasing occurrence of compound heat-humidity (HH) events worldwide, leading to excessive morbidity and mortality. Southeast China has been particularly affected by HH events over the past decade. However, the seasonal prediction of HH events in Southeast China remains a considerable challenge. Based on the year-to-year increment (DY) approach and the empirical orthogonal function analysis, a robust prediction model is developed in this study for the midsummer HH events in Southeast China during 1981–2022. Three prediction models are individually constructed for the first three principal components (PCs) of the DY for HH events (hereafter referred to as HH_DY). The correlation coefficients (CCs) between the predicted and observed PCs exceed 0.75 in the cross-validation test for 1981–2022 and 0.80 in independent hindcasts for 2013–2022. The predicted graphical maps of HH_DY are reconstructed by the projecting the fitted three PCs onto the observed first three empirical orthogonal function modes, and the geographical patterns of HH events are obtained by adding the simulated HH_DY to the observed HH events for the preceding year. In the cross-validation test, the spatial features of the predicted HH events are highly accordant with those of the observations in nine extreme compound HH years, with area CC values ranging from 0.89 to 0.95. In the independent hindcasts for the past decade, the area CC values exceed 0.76 in all years and are >0.80 in eight years. Moreover, the predicted high-incidence areas of HH events are highly consistent with the observations in both the cross-validation test and independent hindcasts. The reliable prediction for geographical distributions of HH events in Southeast China is of great significance for human adaptation to future climate warming.