Seasonal predictability of regional extreme heat depends on how boundary-condition signals shape intrinsic error growth. Here we examine whether May–July (MJJ) Indian Ocean Dipole (IOD) extremes modulate the predictability limit (PL) of summer extreme maximum temperature (TXx) over the North China Plain (NCP). Using ERA5-derived TXx and a nonlinear local Lyapunov exponent framework, we show that both negative and positive IOD extremes slow early TXx error growth relative to neutral IOD conditions. The NCP TXx PL increases from 2.78 months in neutral years to 4.14 months for combined IOD extremes. Composite diagnostics link this extension to organized Indo-Pacific convection, western North Pacific circulation, and local moisture–cloud–radiation constraints. TXx amplitude is not enhanced, indicating that longer predictability arises from constrained local evolution rather than stronger heat anomalies. These results identify MJJ IOD extremes as a boundary-condition context for enhanced seasonal TXx potential predictability.

This study investigates the inter-seasonal variability of predictability limits (PLs) for South China seasonal precipitation (SCSP) using reanalysis data, emphasizing the crucial role of the western North Pacific anomalous anticyclone (WNPAC) in modulating SCSP predictability through ENSO-driven processes. Incorporating the relative global attractor radius as the saturation threshold into the nonlinear local Lyapunov exponent method to address SCSP’s oscillatory error growth after saturation reveals that its PL peaks in winter (3.72 months) and drops to its lowest during the autumn (3.12 months). ENSO is identified as the primary source of SCSP predictability, with its PLs generally extending longer and exhibiting distinct inter-seasonal variability compared to those of SCSP. As a crucial system linking ENSO and SCSP, WNPAC mirrors with SCSP in both PLs’ values and inter-seasonal variability. Physical analysis shows that seasonal shift in the WNPAC-ENSO relationship exerts a strong influence on SCSP predictability.

Exploring the sources of atmospheric predictability plays an important role in enhancing our understanding and improving the level of practical simulation and prediction. El Niño and the Southern Oscillation (ENSO), a prominent external forcing, exerts a profound influence on the global atmospheric system. Under the theoretical guidance of the nonlinear local Lyapunov exponent (NLLE) method and the conditional nonlinear local Lyapunov exponent (CNLLE) method, this work applied the optimal local dynamic analog (OLDA) algorithm to quantify the PLs of geopotential height, air temperature, and wind on a seasonal-to-interannual time scale, while explicitly isolating ENSO’s influence on their predictability. Our analysis reveals that the OLDA algorithm yields superior PL estimates. Specifically, the zonal-mean PLs of tropical mid-upper tropospheric geopotential height reach about 17 months, significantly extending previous estimations. While tropospheric predictability outside the tropics is generally low, notable exceptions exist, with localized low-level wind predictability in Antarctica exceeding 11 months. Through a more clearly response from predictability contributions, ENSO’s impact on the predictability of different variables and at different levels also varies. For example, the zonal averages of ENSO’s predictability contributions to mid-upper tropospheric geopotential height and air temperature have strong positive double peaks at the tropical margins of both hemispheres, while in the lower troposphere, a positive single peak is observed at the equator. Moreover, lower-tropospheric predictability contributions display differing east–west hemispheric asymmetries in geopotential height and temperature patterns. In terms of vertical distribution, the tropical zonal-height high-value distributions of ENSO’s predictability contributions to tropical geopotential height and air temperature present distinctive X-shaped and Y-shaped patterns, respectively. Additionally, ENSO’s predictability influence on wind fields remains constrained in non-divergent layers, such that the characteristics are similar to wind predictability. These results are conducive to enhancing the understanding of atmospheric system’s predictability and providing a helpful reference for practical prediction applications.

As a major environmental hazard, heat events can significantly impact human health, ecosystems, and social services. The accurate prediction of heat events at subseasonal timescales is essential for the mitigation of disasters. Based on observational data and reforecast data retrieved from the North American Multi-Model Ensemble (NMME) project, this study investigated the synergistic effect of the negative SST anomaly (SST–) over the central North Atlantic and the positive precipitation anomaly (PREC+) over the Southern Russian Steppe in June on the subseasonal heat prediction skill and the observed monthly maximum temperature (Tmax) in August over the Yangtze River Basin (YRB). The results first demonstrate that the NMME subseasonal heat prediction is unable to accurately reproduce the response to the forcing of two factors, indicating a lack of precision that NMME could be enhanced. The composite analysis of circulation further illustrates that the joint events can synergistically diminish the YRB Tmax in August through non-linear physical processes and then amplify the NMME subseasonal prediction bias. In particular, the inactive subseasonal wave activity over the mid-latitudes at the upper troposphere, the weakened Western North Pacific subtropical high, and the strengthened cyclonic anomalies at the lower troposphere are the principal processes responsible for the synergistic effect of SST– and PREC+. This study identifies the potential subseasonal precursors for the heat prediction over the YRB. It suggests that advancing subseasonal prediction should consider the synergistic effects of different land and sea signals in the preceding period.

A new air–sea coupled variability of the sea surface temperature anomalies over the tropical Indian Ocean was observed and named as the tropical Indian Ocean tripole (IOT) in terms of its unique tripole pattern in recent studies, which peaks in boreal summer. This study found that the interannual variations of the warm extremes over the Indochina Peninsula during boreal summer can be affected by the IOT. When the positive IOT events occur, two cross-equatorial airflows are induced over the eastern and western tropical Indian Ocean, reinforcing the anomalous warm-humid westerly wind over the northern Indian Ocean. The water vapor convergence is strengthened and the anomalous middle-lower level negative geopotential height is formed over the Indochina Peninsula, leading to the local enhanced ascending motion and surplus precipitation, strongly supported by both observations and numerical simulation. These circulation anomalies favor the reduction of the local surface temperatures through increasing the cloud cover and hindering the downward solar radiation, further declining the warm extremes days. Improving to understand the warm extreme variations over the Indochina Peninsula and its impact factors is of great significance for local industrial development, agricultural production and human health.

Accurately estimating decadal predictability limits (PLs) is essential for advancing long-term climate predictions and understanding decadal-scale variability. This study combines the optimal local dynamic analog (OLDA) algorithm with the nonlinear local Lyapunov exponent (NLLE) method to estimate decadal PLs of oceanic and atmospheric variables, using long-term reanalysis datasets. Results demonstrate that the OLDA algorithm can enhance identification of analog states and improve PL estimation. The decadal PLs of sea surface temperature (SST) show regional and seasonal differences, with zonal mean values ranging from 8 to 17 years, and higher values in boreal summer and autumn, especially in the Northern Hemisphere and Southern Ocean. Sea level pressure (SLP) decadal PLs range from 8 to 11 years, exhibiting patchy distribution and seasonal variation. The global mean PL of SLP reaches about 10 years in boreal spring and 9 years in other seasons. SLP and SST PL distributions differ across seasons, reflecting the complexity of ocean–atmosphere interactions. Decadal PLs of major climate modes were also estimated, e.g., decadal PL of the SST Inter-Hemispheric Dipole (SSTID) is 17 years, Atlantic Multidecadal Oscillation (AMO) 14 years, Pacific Decadal Oscillation (PDO) 13 years, North Atlantic Oscillation (NAO) 16 years, Northern Hemisphere Annular Mode (NAM) 11 years, and Southern Hemisphere Annular Mode (SAM) 15 years. These modes display distinct predictability patterns and seasonal variations, highlighting their unique roles in regional climate dynamics. These findings enhance our understanding of decadal-scale predictability.

This study investigated the influence of interannual variations in tropical Indian Ocean tripole (IOT) on the surface air temperature (SAT) over the western Tibetan Plateau (TP) during boreal summer. During the positive phase of the IOT, two northward cross-equatorial airflows are induced over the tropical eastern and western Indian Ocean. These airflows reinforce the ascending motion over the southern tropical Asia (15°–25°N, 80°–125°E), increasing local precipitation, as confirmed by observations and simulations by the Community Atmosphere Model. The upper-level Asian Continental Meridional Teleconnection (ACMT) pattern is excited by the latent heat released from precipitation and transmits signals from the southern tropical Asia to the western TP, leading to the positive geopotential height anomalies and anomalous anticyclones over there. Upper-level circulation anomalies over the western TP enhance atmospheric thickness through adiabatic processes, consequently elevating local SAT. The ACMT associated with precipitation anomalies thus serves as an atmospheric bridge connecting the IOT and the SAT variations over the western TP.

The Indian Ocean dipole (IOD) is a remarkable interannual variability in the tropical Indian Ocean. The improved prediction of IOD is of a great value because of its large socioeconomic impacts. Previous studies reported that both El Niño-Southern Oscillation (ENSO) and South China Sea summer monsoon (SM) play a dominant role in the western and eastern pole of the IOD, respectively. They can be used as predictors of the IOD at 3 month lead beyond self-persistence. Here, we develop an empirical model of multi-factors in which the western pole is predicted by ENSO and persistence and the eastern pole is predicted by SM and persistence. This new empirical model outperforms largely the average level of the dynamical models from the North American multi-model ensemble (NMME) project in predicting the peak IOD in boreal autumn, with a correlation coefficient of ∼0.86 and a root mean square error of ∼0.24 °C. Furthermore, the hit rate of positive culminated IOD in this new empirical model is equivalent to that in current NMME models (above 65%), much higher than that for negative culminated IOD. This improvement of skill using the empirical model suggests a perspective for better understanding and predicting the IOD.

Accurately estimating decadal predictability limits (PLs) is essential for advancing long-term climate predictions and understanding decadal-scale variability. This study combines the optimal local dynamic analog (OLDA) algorithm with the nonlinear local Lyapunov exponent (NLLE) method to estimate decadal PLs of oceanic and atmospheric variables, using long-term reanalysis datasets. Results demonstrate that the OLDA algorithm can enhance identification of analog states and improve PL estimation. The decadal PLs of sea surface temperature (SST) show regional and seasonal differences, with zonal mean values ranging from 8 to 17 years, and higher values in boreal summer and autumn, especially in the Northern Hemisphere and Southern Ocean. Sea level pressure (SLP) decadal PLs range from 8 to 11 years, exhibiting patchy distribution and seasonal variation. The global mean PL of SLP reaches about 10 years in boreal spring and 9 years in other seasons. SLP and SST PL distributions differ across seasons, reflecting the complexity of ocean-atmosphere interactions. Decadal PLs of major climate modes were also estimated, e.g., decadal PL of the SST Inter-Hemispheric Dipole (SSTID) is ~ 17 years, Atlantic Multidecadal Oscillation (AMO) ~ 14 years, Pacific Decadal Oscillation (PDO) ~ 13 years, North Atlantic Oscillation (NAO) ~ 16 years, Northern Hemisphere Annular Mode (NAM) ~ 11 years, and Southern Hemisphere Annular Mode (SAM) ~ 15 years. These modes display distinct predictability patterns and seasonal variations, highlighting their unique roles in regional climate dynamics. These findings enhance our understanding of decadal-scale predictability.

Abstract The North Pacific sea surface temperature (SST) has a profound climatic influence. The El Niño‐Southern Oscillation (ENSO) significantly impacts the North Pacific SST; however, the influence of the distinct phases of ENSO on SST predictability remains unclear. To overcome the model limitations, this study assessed SST predictability under diverse ENSO phases using reanalysis. The predictability limit of the North Pacific SST under La Niña (8.4 months) is longer than that under Neutral (5.9 months) and El Niño (5.5 months) conditions, which unveils asymmetry. This asymmetry mirrors contemporary multimodal prediction skills. Error growth dynamics reveal La Niña's robust signal strength with a slow error growth rate, in contrast to El Niño's weaker signal and faster error growth. There exhibits intermediate signal strength and elevated error growth in Neutral condition. Physically, predictability signal strength aligns with SST variability, whereas the error growth rate correlates with atmospheric‐ocean heating anomalies. La Niña, which induces positive heating anomalies, minimizes the impact of atmospheric noise, resulting in lower error growth. The result is beneficial for improving North Pacific SST predictions.

Winter precipitation anomalies in South China (SC) frequently result in severe disasters. However, the evaluation of prediction performance and distinctions between positive precipitation anomaly events (PPA, wet condition) and negative precipitation anomaly events (NPA, dry condition) in current operational models remains incomplete. This study employed the Climate Forecast System version 2 (CFSv2) to assess winter precipitation prediction accuracy in SC from 1983 to 2021. Differences in predicting PPA and NPA events and the underlying physical mechanisms were explored. The results indicate that CFSv2 can effectively predict interannual variations in winter precipitation in SC, as there is a significant time correlation coefficient of 0.68 (0.62) between observations and predictions, with a lead time of 0 (3) months. The model revealed an intriguing asymmetry in prediction skills: PPA outperformed NPA in both deterministic and probabilistic prediction. The higher predictability of PPA, as indicated by the perfect model correlation and signal-to-noise ratio, contributed to its superior prediction performance when compared to NPA. Physically, tropical signals from the ENSO and extratropical signals from the Arctic Sea ice anomaly, were found to play pivotal roles in this asymmetric feature. ENSO significantly impacts PPA events, whereas NPA events are influenced by a complex interplay of factors involving ENSO and Arctic Sea ice, leading to low NPA predictability. The capability of the model to replicate Arctic Sea ice signals is limited, but it successfully predicts ENSO signals and reproduces their related circulation responses. This study highlights the asymmetrical features of precipitation prediction, aiding in prediction models improvement.

We use short‐range ensemble forecasts and ensemble clustering analysis to study the factors affecting the intensification of Hurricane Patricia (2015). Convection‐permitting ensemble forecasts are classified into two groups: 10 spin‐down (SPD) members and 10 spin‐up (SPU) members with intensification rates of <0 and >0 m s −1 for the first 6 hr, respectively. Ensemble clustering analysis found that the wind–pressure relationship was incorrect in the SPD group, indicating that the SPD issue may be partly caused by an imbalance in the initial minimum sea‐level pressure (MSLP) and the maximum wind speed (MWS). The SPD issue appears to be related to three main points: (a) a weaker upper‐level warm core; (b) a drier inner core at low levels; and (c) a dry, cold air intrusion at mid‐levels. In contrast, the SPU group has a stronger upper‐level warm core and a relatively wet inner core at lower levels, as well as a relatively strong secondary circulation. These favorable initial conditions in the SPU group, combined with a greater updraft and stronger convection around the eyewall, result in more latent heating around or in the eyewall that favors the intensification of the tropical cyclone. Comparisons between the SPD and SPU groups suggest that the analyzed ensemble could not accurately capture the relationship between the initial MSLP and MWS, which, combined with the unfavorable thermodynamic conditions at the initial time, resulted in the incorrect evolution of the intensity. Therefore, improving the initial conditions appears to be an effective way to address the SPD issue.

Abstract This study focuses on regional extreme precipitation (REP) in North China. We found a trend turning in summer (July and August) REP frequencies and intensities from a decrease trend in 1961-2002 to an increase trend in 2003-2020, accompanying which, the extreme rain belt shows a southward shift, and the connections of the REP with Ural blocking and with the western Pacific subtropical high (WPSH) are enhanced in 2003-2020. The low and high pressures at west and east of North China (the low-high dipole, LHD) leads to northward moisture transport, air rising and rain there. During the REP the high pressure of the LHD at Northeast China (the NEH) is strongly amplified through the forcing of Rossby wave energy propagating along the zonal subpolar/subtropical wave guide over Eurasian on the preexisting northeast Asia stationary ridge. In 2003-2020, the enhancement of Ural stationary ridge favors the development of Ural blockings (UB), and leads to a change of the eastward Rossby wave propagating path from along the subtropical wave guide in 1961-2002 to along the polar wave guide in 2003-2020, which connects the UB and the LHD, leads to a strengthened-and- northeastward-shift/southwestward-shift of the low/high of the LHD. The JA mean WPSH expands more westward-northward in 2003-2020 than in 1961-2002. It provides a condition for a further westward-northward expanding of the daily WPSH under the influence of the NEH before the REP, which brings strong moisture from north Pacific and results in a strengthening of extreme rain over southern North China.

This study investigates the role of perturbation potential energy (PPE) in energetic connection between the South China Sea summer monsoon (SCSSM) and Indian Ocean dipole (IOD). When the SCSSM is strong during boreal summer, the higher and lower PPE anomalies controlled primarily by the diabatic heating correspond to negative and positive energy conversion, favoring the ascending and descending motions over western North Pacific (WNP) and southern Maritime Continent (SMC), respectively. This implies the existence of the regional Hadley circulation. This regional Hadley circulation-induced lower southeasterly wind anomalies reduce the local sea surface temperature (SST) anomalies over the tropical southeastern Indian Ocean via the wind–evaporation–SST and wind–thermocline–SST feedbacks, increasing the zonal SST gradient over the tropical Indian Ocean. Thus, a positive IOD event develops in boreal summer, and verse vice. Although the SCSSM decays during boreal autumn, the increased gradient of the PPE anomalies intensifies the anomalous Walker circulation over the tropical Indian Ocean, providing positive feedback that allows the IOD to mature. Consequently, the PPE dipole over WNP and SMC serves as an energetic bridge between the SCSSM and IOD.

This study focuses on regional extreme precipitation (REP) in North China. We found a trend turning in summer (July to August) REP frequencies and intensities from a decrease trend in 1961–2002 to an increase trend in 2003–2020, accompanied by a southward shift of the extreme rain belt, and an enhanced connection with the Ural blocking (UB) and the Western Pacific Subtropical High Pressure (WPSH) in 2003–2020. Rains in North China are accompanied by a west–east low–high dipole at upper troposphere. During the REP, the high of the low–high dipole at Northeast China (the NEH) is strongly amplified from a pre-existing stationary ridge over northeast Asia under the influence of eastward propagating Rossby wave energies along the subpolar/subtropical wave guide over Eurasia. For the REP years, an enhanced stationary ridge over the Ural Mountains in the period 2003–2020 replaces the stationary Ural trough in the period 1961–2002, favouring the development of the UB and leading to a change of the Rossby wave propagation path from along the subtropical waveguide in 1961–2002 to along the polar wave guide in 2003–2020. Therefore, a connection between the NEH and the UB forms, which may lead to a higher probability of extreme precipitation in North China since blocking is a major source of strong circulation anomalies. The mean summer WPSH expands more westward-northward in 2003–2020 than in 1961–2002, which provides a background conditions for a further westward-northward expanding of the daily WPSH under the influence of the NEH leading to a strong moisture transport from north Pacific. As a result, the intensity and probability of extreme precipitation over southern North China increase.

This paper examines the differences in the troposphere-stratosphere coupling during extreme and moderate El Niño events by considering the wave geometry of the stratospheric vortex, using two-dimensional moment diagnostics to measure the geometry of the vortex. Based on reanalysis data for 1979–2020 and sensitivity runs with the Whole Atmosphere Community Climate Model version 4 (WACCM4), we demonstrate that the Arctic polar vortex exhibits larger variability throughout the late winter to the early spring season in terms of the vortex geometry during extreme El Niño than that during moderate El Niño. Although both equatorward displacement and splitting of the vortex are detected during moderate and extreme El Niño events, split vortex events show larger occurrences than displaced vortex events in the case of extreme El Niño. This is in agreement with stronger tropospheric responses to extreme El Niño manifested in zonal mean signals in the tropospheric jet stream and surface temperature. The geometric characteristics of the vortex, being incoherent with the zonal mean results, show that disturbed vortex during extreme El Niño penetrate deeper into the troposphere, while anomalies associated with moderate El Niño do not decent far below the tropopause.

This study investigates the influence of the interannual variability of Indian Ocean tripole (IOT) on summertime cold extremes in Central Siberia. During positive IOT phases, two cross‐equatorial airflows are induced over the tropical eastern and western Indian Ocean. These strengthen ascending motion over the southern tropical Asia (80°–125°E, 15°–25°N), increasing precipitation in situ, as evidenced in observations and simulations by using Community Atmosphere Model. Serving as a heat source, the induced upper‐level Asian continent meridional teleconnection (ACMT) pattern transports the signals from southern tropical Asia into Central Siberia. Positive upper‐level geopotential height anomalies over Central Siberia induced by ACMT favor more solar radiation to reach the surface and raise local surface temperatures through modulating the tropospheric air expansion/compression, further reducing the extreme cold days. Consequently, the ACMT induced by latent heat fluxes associated with precipitation anomalies acts as an atmospheric bridge that links the IOT to Central Siberia cold extremes.

El Niño and the Southern Oscillation (ENSO) have a worldwide impact on seasonal to yearly climate. However, there are decadal variations in the seasonal prediction skill of ENSO in dynamical and statistical models; in particular, ENSO prediction skill has declined since 2000. The shortcomings of models mean that it is very important to study ENSO seasonal predictability and its decadal variation using observational/reanalysis data. Here we quantitatively estimate the seasonal predictability limit (PL) of ENSO from 1900 to 2015 using Nonlinear local Lyapunov exponent (NLLE) theory with an observational/reanalysis dataset and explore its decadal variations. The mean PL of sea surface temperature (SST) is high in the central/eastern tropical Pacific and low in the western tropical Pacific, reaching 12–15 and 7–8 months, respectively. The PL in the tropical Pacific varies on a decadal timescale, with an interdecadal standard deviation of up to 2 months in the central tropical Pacific that has similar spatial structure to the mean PL. Taking the PL of SST in the Niño 3.4 region as representative of the PL in the central/eastern tropical Pacific, there are clearly higher values in the 1900s, mid-1930s, mid-1960s, and mid-1990s, and lower values in the 1920s, mid-1940s, and mid-2010s. Meanwhile, the PL of SST in the Niño 6 region—whose average value is 7 months—is in good agreement with the PL of most regions in the western tropical Pacific, with higher values in the 1910s, 1940s, and 1980s and lower values in the 1930s, 1950s, and mid-1990s. In the framework of NLLE theory, the PL is determined by the error growth rate (representing the dissipation rate of the predictable signal) and the saturation value of relative error (representing predictable signal intensity). We reveal that the spatial structure of the mean PL in the tropical Pacific is determined mainly by the error growth rate. The decadal variability of PL is affected more by the variation of the saturation value of relative error in the equatorial Pacific, whereas the error growth rate cannot be ignored in the PL of some regions. As an important source of predictability in ENSO dynamics, the relationship between warm water volume and SST in the Niño 3.4 region has a critical role in the decadal variability of PL in the tropical Pacific through the error growth rate and saturation value of relative error. This strong relationship reduces the error growth rate in the initial period and increases the saturated relative error, contributing to the high PL.

The Indian Ocean tripole (IOT) is an independent mode of ocean–atmosphere circulation centered on the tropical Indian Ocean. This study explores the physical mechanisms of the IOT affecting the western United States climate variation during the boreal summer. We find that the IOT is significantly correlated with both western United States summer surface temperature and precipitation anomalies. During positive IOT events, the westerly wind anomalies over the northern Indian Ocean are intensified by two cross-equator airflows over the tropical eastern Indian Ocean and the east coast of Africa. The resulting convergence of air over the northern Bay of Bengal–Indochina Peninsula–northern South China Sea (NBB–IP–NSCS) region (80°–125°E, 15°–25°N) exacerbates the surplus precipitation there. Serving as a heat source, these NBB–IP–NSCS precipitation anomalies can excite a circum-global teleconnection-like (CGT–like) pattern that propagates eastward from west-central Asia towards North America along the Asia subtropical westerly jet, further influencing local circulation anomalies. Development of strong anticyclonic circulation over the western United States enhances descending motion and divergence there, resulting in negative precipitation anomalies. This circulation anomaly also induces the diabatic heating anomalies through allowing more solar radiation to reach the ground surface, further increasing the surface temperature anomalies. Meanwhile, the increased tropospheric temperature also raises local surface temperatures by modulating the adiabatic air expansion and compression. Ultimately, the CGT-like pattern associated with NBB–IP–NSCS precipitation anomalies sets up an atmospheric bridge by which the IOT can impact summer climate in the western United States.

The 2-m air temperature (T2m) is an important meteorological variable and has been the focus of meteorological forecasting. Although the numerical weather model is an important means of forecasting, it typically presents forecasting errors that cannot be eliminated by improving the ability of the numerical model to reproduce the processes. Thus, a statistical correction of the forecast results is required. In this study, we applied the local dynamical analog (LDA) method to correct the operational T2m forecast product obtained from the European Centre for Medium-Range Weather Forecasts with the lead time of 24-240 h. To our knowledge, for the first time, we used spatially adjacent grids from high-resolution grid data as potential analog pools to compensate for the short duration of historical data. The T2m of weather forecasts in East Asia for December 2018 was improved by LDA correction with a small sample condition. Compared with ERA5 and station observation data, the results show that the root mean square error can be reduced by 2%-4% and the correlation coefficient can be increased by 1%-5% for different lead times, with the most distinct improvement effect for the medium-term forecast time. The Qinghai Tibet Plateau, Mongolia Plateau, and other areas, where the raw prediction error is relatively high, presented better performance than other regions. For a cold-wave process, we also demonstrate that the corrected results based on analogs present better forecasting skill performance than raw forecast results. The analog correction with the LDA method, which combines statistical and model dynamical techniques, is proposed to be integrated with other advanced operational models. The forecast skill of T2m was improved by a historical dataset, which may contribute to energy management and the construction industry.