Evaluation of statistical bias correction methods for numerical weather prediction model forecasts of maximum and minimum temperatures

Statistical bias correction methods for numerical weather prediction (NWP) forecasts of maximum and minimum temperatures over India in the medium-range time scale (up to 5 days) are proposed in this study. The objective of bias correction is to minimize the systematic error of the next forecast using bias from past errors. The need for bias corrections arises from the many sources of systematic errors in NWP modeling systems. NWP models have shortcomings in the physical parameterization of weather events and have the inability to handle sub-grid phenomena successfully. The statistical algorithms used for minimizing the bias of the next forecast are running-mean (RM) bias correction, best easy systematic estimator, simple linear regression and the nearest neighborhood (NN) weighted mean, as they are suitable for small samples. Bias correction is done for four global NWP model maximum and minimum temperature forecasts. The magnitude of the bias at a grid point depends upon geographical location and season. Validation of the bias correction methodology is carried out using daily observed and bias-corrected model maximum and minimum temperature forecast over India during July–September 2011. The bias-corrected NWP model forecast generally outperforms direct model output (DMO). The spatial distribution of mean absolute error and root-mean squared error for bias-corrected forecast over India indicate that both the RM and NN methods produce the best skill among other bias correction methods. The inter-comparison reveals that statistical bias correction methods improve the DMO forecast in terms of accuracy in forecast and have the potential for operational applications.     

Operational forecasting of daily summer maximum and minimum temperatures in the Valencia Region

Extreme-temperature events have a great impact on human society. Thus, knowledge of summer temperatures can be very useful both for the general public and for organizations whose workers operate in the open. An accurate forecasting of summer maximum and minimum temperatures could help to predict heatwave conditions and permit the implementation of strategies aimed at minimizing the negative effects that high temperatures have on human health. The objective of this work is to evaluate the skill of the regional atmospheric and modelling system (RAMS) model in determining daily summer maximum and minimum temperatures in the Valencia Region. For this, we have used the real-time configuration of this model currently running at the Centro de Estudios Ambientales de Mediterráneo Foundation. This operational system is run twice a day, and both runs have a 3-day forecast range. To carry out the verification of the model in this work, the information generated by the system has been broken into individual simulation days for a specific daily run of the model. Moreover, we have analysed the summer forecast period from 1 June to 31 August for 2007, 2008, 2009 and 2010. The results indicate good agreement between observed and simulated maximum temperatures, with RMSE in general near 2 °C both for coastal and inland stations. For this parameter, the model shows a negative bias around ?1.5 °C in the coast, while the opposite trend is observed inland. In addition, RAMS also shows good results in forecasting minimum temperatures for coastal locations, with bias lower than 1 °C and RMSE below 2 °C. However, the model presents some difficulties for this parameter inland, where bias higher than 3 °C and RMSE of about 4 °C have been found. Besides, there is little difference in both temperatures forecasted within the two daily RAMS cycles and that RAMS is very stable in maintaining the forecast performance at least for three forecast days.     

Trends in seasonal temperatures over the Indian region

An investigation has been carried out to identify the trends in maximum, minimum and mean temperatures and temperature range over the Indian land mass during the winter (January, and February), pre-monsoon (March–May), southwest monsoon (June–September) and post-monsoon (October–December) seasons by using high resolution daily gridded data set prepared by India Meteorological Department for the period of 1969–2005. It has been observed that the maximum temperatures over the west coast of India show rising trend in winter, southwest monsoon and post-monsoon seasons but the maximum temperatures do not show any significant trend over the other parts of the country. Minimum temperatures show increasing trend over the North Indian states in all seasons and they show an increasing trend over the west coast of India in winter and southwest monsoon seasons. Mean temperature shows an increasing trend over the west coast of India during winter and southwest monsoon seasons. Decreasing trend is observed in the temperature range over North India in all seasons due to increasing trend in minimum temperature.     

Experimental real-time multi-model ensemble (MME) prediction of rainfall during monsoon 2008: Large-scale medium-range aspects

Realistic simulation/prediction of the Asian summer monsoon rainfall on various space–time scales is a challenging scientific task. Compared to mid-latitudes, a proportional skill improvement in the prediction of monsoon rainfall in the medium range has not happened in recent years. Global models and data assimilation techniques are being improved for monsoon/tropics. However, multi-model ensemble (MME) forecasting is gaining popularity, as it has the potential to provide more information for practical forecasting in terms of making a consensus forecast and handling model uncertainties. As major centers are exchanging model output in near real-time, MME is a viable inexpensive way of enhancing the forecasting skill and information content. During monsoon 2008, on an experimental basis, an MME forecasting of large-scale monsoon precipitation in the medium range was carried out in real-time at National Centre for Medium Range Weather Forecasting (NCMRWF), India. Simple ensemble mean (EMN) giving equal weight to member models, bias-corrected ensemble mean (BCEMn) and MME forecast, where different weights are given to member models, are the products of the algorithm tested here. In general, the aforementioned products from the multi-model ensemble forecast system have a higher skill than individual model forecasts. The skill score for the Indian domain and other sub-regions indicates that the BCEMn produces the best result, compared to EMN and MME. Giving weights to different models to obtain an MME product helps to improve individual member models only marginally. It is noted that for higher rainfall values, the skill of the global model rainfall forecast decreases rapidly beyond day-3, and hence for day-4 and day-5, the MME products could not bring much improvement over member models. However, up to day-3, the MME products were always better than individual member models.     

Optimisation of Hidden Markov Model using Baum–Welch algorithm for prediction of maximum and minimum temperature over Indian Himalaya

Maximum and minimum temperatures are used in avalanche forecasting models for snow avalanche hazard mitigation over Himalaya. The present work is a part of development of Hidden Markov Model (HMM) based avalanche forecasting system for Pir-Panjal and Great Himalayan mountain ranges of the Himalaya. In this work, HMMs have been developed for forecasting of maximum and minimum temperatures for Kanzalwan in Pir-Panjal range and Drass in Great Himalayan range with a lead time of two days. The HMMs have been developed using meteorological variables collected from these stations during the past 20 winters from 1992 to 2012. The meteorological variables have been used to define observations and states of the models and to compute model parameters (initial state, state transition and observation probabilities). The model parameters have been used in the Forward and the Viterbi algorithms to generate temperature forecasts. To improve the model forecasts, the model parameters have been optimised using Baum–Welch algorithm. The models have been compared with persistence forecast by root mean square errors (RMSE) analysis using independent data of two winters (2012–13, 2013–14). The HMM for maximum temperature has shown a 4–12% and 17–19% improvement in the forecast over persistence forecast, for day-1 and day-2, respectively. For minimum temperature, it has shown 6–38% and 5–12% improvement for day-1 and day-2, respectively.     

Trend analysis of rainfall pattern over the Central India during 1901–2010

In the present study, trends of rainfall of the Central India were evaluated in monthly, seasonal, and annual time scales using the Revised Mann-Kendall (RMK) test, Sen’s slope estimator, and innovative trend method (ITM). For this purpose, the monthly rainfall data for 20 stations in Madhya Pradesh (MP) and Chhattisgarh (CG) states in Central India during 1901–2010 was used. The Sen’s slope estimator was utilized for calculating the slope of rainfall trend line. Based on the obtained results of RMK test, there is no significant trend in the stations for the January and October months. The results also showed that for MP, two out of 15 considered stations indicate significant annual trend, while the CG has four out of five stations with significant trend. The results of applying ITM test indicated that most of the stations have decreasing trends in annual (16 stations), summer (16 stations), and monsoon (11 stations) seasons, while the winter (12 stations) and post monsoon (11 stations) seasons generally show increasing trend. Unlike the RMK, the ITM shows significant increasing trend in rainfall of November and December months. The finding of current study can be used for irrigation and water resource management purpose over the Central India.     

Prediction and error growth in the daily forecast of precipitation from the NCEP CFSv2 over the subdivisions of Indian subcontinent

This study investigates the forecast skill and predictability of various indices of south Asian monsoon as well as the subdivisions of the Indian subcontinent during JJAS season for the time domain of 2001–2013 using NCEP CFSv2 output. It has been observed that the daily mean climatology of precipitation over the land points of India is underestimated in the model forecast as compared to observation. The monthly model bias of precipitation shows the dry bias over the land points of India and also over the Bay of Bengal, whereas the Himalayan and Arabian Sea regions show the wet bias. We have divided the Indian landmass into five subdivisions namely central India, southern India, Western Ghat, northeast and southern Bay of Bengal regions based on the spatial variation of observed mean precipitation in JJAS season. The underestimation over the land points of India during mature phase was originated from the central India, southern Bay of Bengal, southern India and Western Ghat regions. The error growth in June forecast is slower as compared to July forecast in all the regions. The predictability error also grows slowly in June forecast as compared to July forecast in most of the regions. The doubling time of predictability error was estimated to be in the range of 3–5 days for all the regions. Southern India and Western Ghats are more predictable in the July forecast as compared to June forecast, whereas IMR, northeast, central India and southern Bay of Bengal regions have the opposite nature.     

Trend analysis and change point detection of annual and seasonal precipitation and temperature series over southwest Iran

This paper presents results of trend analysis and change point detection of annual and seasonal precipitation, and mean temperature (TM), maximum temperature (TMAX) and minimum temperature (TMIN) time series of the period 1950–2007. Investigations were carried out for 50 precipitation stations and 39 temperature stations located in southwest Iran. Three statistical tests including Pettitt’s test, Sequential Mann–Kendall test (SQ-MK test) and Mann–Kendall rank test (MK-test) were used for the analysis. The results obtained for precipitation series indicated that most stations showed insignificant trends in annual and seasonal series. Out of the stations which showed significant trends, highest numbers were observed during winter season while no significant trends were detected in summer precipitation. Moreover, no decreasing significant trends were detected by statistical tests in annual and seasonal precipitation series. The analysis of temperature trends revealed a significant increase during summer and spring seasons. TMAX was more stable than TMIN and TM, and winter was stable compared to summer, spring and autumn seasons. The results of change point detection indicated that most of the positive significant mutation points in TM, TMAX and TMIN began in the 1990s.     

Prediction of fog/visibility over India using NWP Model

Frequent occurrence of fog in different parts of northern India is common during the winter months of December and January. Low visibility conditions due to fog disrupt normal public life. Visibility conditions heavily affect both surface and air transport. A number of flights are either diverted or cancelled every year during the winter season due to low visibility conditions, experienced at different airports of north India. Thus, fog and visibility forecasts over plains of north India become very important during winter months. This study aims to understand the ability of a NWP model (NCMRWF, Unified Model, NCUM) with a diagnostic visibility scheme to forecast visibility over plains of north India. The present study verifies visibility forecasts obtained from NCUM against the INSAT-3D fog images and visibility observations from the METAR reports of different stations in the plains of north India. The study shows that the visibility forecast obtained from NCUM can provide reasonably good indication of the spatial extent of fog in advance of one day. The fog intensity is also predicted fairly well. The study also verifies the simple diagnostic model for fog which is driven by NWP model forecast of surface relative humidity and wind speed. The performance of NWP model forecast of visibility is found comparable to that from simple fog model driven by NWP forecast of relative humidity and wind speed.     

Evaluation of the weather research and forecasting (WRF) model over Guyana

In the present study, diagnostic studies were undertaken using station-based rainfall data sets of selected stations of Guyana to understand the variability of rainfall. The multidecadal variation in rainfall of coastal station Georgetown and inland station Timehri has shown that the rainfall variability was less during the May–July (20–30%) of primary wet season compared to the December--January (60–70%) of second wet season. The rainfall analysis of Georgetown based on data series from 1916 to 2007 shows that El Niño/La Niña has direct relation with monthly mean rainfall of Guyana. The impact is more predominant during the second wet season December--January. A high-resolution Weather Research and Forecasting model was made operational to generate real-time forecasts up to 84 h based on 00 UTC global forecast system (GFS), NCEP initial condition. The model real-time rainfall forecast during July 2010 evaluation has shown a reasonable skill of the forecast model in predicting the heavy rainfall events and major circulation features for day-to-day operational forecast guidance. In addition to the operational experimental forecast, as part of model validation, a few sensitivity experiments are also conducted with the combination of two cloud cumulus (Kain--Fritsch (KF) and Betts–Miller–Janjic (BMJ)) and three microphysical schemes (Ferrier et al. WSM-3 simple ice scheme and Lin et al.) for heavy rainfall event occurred during 28–30 May 2010 over coastal Guyana and tropical Hurricane ‘EARL’ formed during 25 August–04 September 2010 over east Caribbean Sea. It was observed that there are major differences in the simulations of heavy rainfall event among the cumulus schemes, in spite of using the same initial and boundary conditions and model configuration. Overall, it was observed that the combination of BMJ and WSM-3 has shown qualitatively close to the observed heavy rainfall event even though the predicted amounts are less. In the case of tropical Hurricane ‘EARL’, the forecast track in all the six experiments based on 00 UTC of 28 August 2010 initial conditions for the forecast up to 84 h has shown that the combination of KF cumulus and Ferrier microphysics scheme has shown less track errors compared to other combinations. The overall average position errors for all the six experiments taken together work out to 103 km in 24, 199 km in 48, 197 km in 72 and 174 km in 84 h.     

Weather forecasting for dinosaurs

A weather forecast for 22 September 150 million years ago: 'Most of Britain will continue to enjoy a sunny end to the summer. Winds remain calm, but some cloud may develop by the end of the day. Further afield, India and Australia are experiencing some stormy weather with severe gales across the northern coast of India.'     

Skills of different mesoscale models over Indian region during monsoon season: Forecast errors

Performance of four mesoscale models namely, the MM5, ETA, RSM and WRF, run at NCMRWF for short range weather forecasting has been examined during monsoon-2006. Evaluation is carried out based upon comparisons between observations and day-1 and day-3 forecasts of wind, temperature, specific humidity, geopotential height, rainfall, systematic errors, root mean square errors and specific events like the monsoon depressions.It is very difficult to address the question of which model performs best over the Indian region? An honest answer is ‘none’. Perhaps an ensemble approach would be the best. However, if we must make a final verdict, it can be stated that in general, (i) the WRF is able to produce best All India rainfall prediction compared to observations in the day-1 forecast and, the MM5 is able to produce best All India rainfall forecasts in day-3, but ETA and RSM are able to depict the best distribution of rainfall maxima along the west coast of India, (ii) the MM5 is able to produce least RMSE of wind and geopotential fields at most of the time, and (iii) the RSM is able to produce least errors in the day-1 forecasts of the tracks, while the ETA model produces least errors in the day-3 forecasts.     

东南极中山站-昆仑站断面最高和最低气温变化特征

利用中山站-昆仑站断面自动气象站2 m气温和同期再分析资料分析了南极沿海到内陆高原的最高和最低气温的变化特征, 并通过个例讨论了气温出现极端过程的天气背景. 结果表明: 南极中山站-昆仑站断面最高和最低气温季节变化趋势基本相似, 年际变化不明显. 最高气温标准偏差大于最低气温, 冬季气温标准偏差明显大于夏季, 且夏季气温变化幅度远小于冬季. 随海拔的增加, 最高气温年较差逐渐增大, 最低气温年变化的无芯率(气温没有明显的最小值程度)呈增大趋势, 夏季气温变化幅度逐渐增大, 冬季气温变化幅度的区域性差异不明显; 2005年7月25-31日的极端降温过程主要受到极涡、地面冷高压及下降风的共同影响.     

Tropospheric NO2 variability over the metropolitan cities and hill stations of India

Monthly and inter-annual variation in tropospheric nitrogen dioxide (NO₂) have been examined over metropolitan cities (New Delhi, Kolkata, Mumbai and Chennai) and hill stations (Mount Abu, Nainital, Srinagar, Kodaikanal, Dalhousie, Gulmarg, Shimla and Munnar) of India during the period 2004?C2010 using satellite-based SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY). It is observed that the monthly variation in NO₂ over the metropolitan cities is higher during winter (November?CDecember?CJanuary?CFebruary) months and lower during summer monsoon (June?CJuly?CAugust?CSeptember) months. Lower NO₂ in summer monsoon leads to the presence of deep convection and higher in winter leads to calm winds and more residential time of gases. Moreover, rapid industrialization and traffic growth are also responsible for the higher NO₂. Mean values of NO₂ over New Delhi and Mumbai as well as hill stations, such as Mount Abu, Nainital and Shimla, have exhibited more pollution. Similarly, maximum NO₂ occurred over the hill stations during pre-monsoon months (April?CMay) and early part of summer monsoon (June). Higher NO₂ values are observed in November?CDecember months. All the hill stations also show increasing trend of NO₂ during the period 2004?C2010. Increasing pollution of NO₂ over the hill stations might also be due to forest fires, biomass burning and long-range transport. Back trajectory analysis shows that the observed peaks in NO₂ are a resultant of the long-range transported component amplified by the local environment. In the northern hill stations, pollution seems transported from west Asian and European countries while in the southern hill stations, pollution is originated from southern Indian Ocean and East Asian countries.     

Seasonal Dependence of the North Pacific and North Atlantic SST Predictability and Forecast Skill

In this paper, the decadal predictability and forecast skill of the Sea Surface Temperature Anomalies (SSTA) in the North Pacific and North Atlantic Ocean were investigated by conducting three sets of perfect model forecast experiments using a global coupled general circulation model. The results show that the annual mean SSTA in the North Pacific is less predictable on decadal time scale, with the forecast skill notably weaker than that of the North Atlantic. By analyzing the predictability and forecast skill of seasonal mean SSTA, it is found that the decadal predictability and forecast skill of the winter mean (JFM) SSTA in the central and western North Pacific are significantly higher than those of other seasons, and the magnitude is comparable with that of the North Atlantic. The predictability and forecast skill of the North Atlantic SSTA also show seasonal variations. Further analysis indicates that the seasonal dependence of the SSTA decadal predictability and forecast skill in the North Pacific is due to the winter-to-winter reemergence mechanism of SSTA in the North Pacific, which results from the seasonal variation of the mixed layer depth of the North Pacific Ocean. While the seasonal dependence of the North Atlantic SSTA predictability and forecast skill might be related to seasonal variations of other processes, such as the Atlantic Decadal Oscillation. The results of this paper suggest that for decadal climate prediction, if the forecast skill of the seasonal mean is taken into account, we might obtain higher than annual mean forecast skill for some seasons.     

Impact of vegetation on the simulation of seasonal monsoon rainfall over the Indian subcontinent using a regional model

The change in the type of vegetation fraction can induce major changes in the local effects such as local evaporation, surface radiation, etc., that in turn induces changes in the model simulated outputs. The present study deals with the effects of vegetation in climate modeling over the Indian region using the MM5 mesoscale model. The main objective of the present study is to investigate the impact of vegetation dataset derived from SPOT satellite by ISRO (Indian Space Research Organization) versus that of USGS (United States Geological Survey) vegetation dataset on the simulation of the Indian summer monsoon. The present study has been conducted for five monsoon seasons (1998–2002), giving emphasis over the two contrasting southwest monsoon seasons of 1998 (normal) and 2002 (deficient). The study reveals mixed results on the impact of vegetation datasets generated by ISRO and USGS on the simulations of the monsoon. Results indicate that the ISRO data has a positive impact on the simulations of the monsoon over northeastern India and along the western coast. The MM5-USGS has greater tendency of overestimation of rainfall. It has higher standard deviation indicating that it induces a dispersive effect on the rainfall simulation. Among the five years of study, it is seen that the RMSE of July and JJAS (June–July–August–September) for All India Rainfall is mostly lower for MM5-ISRO. Also, the bias of July and JJAS rainfall is mostly closer to unity for MM5-ISRO. The wind fields at 850 hPa and 200 hPa are also better simulated by MM5 using ISRO vegetation. The synoptic features like Somali jet and Tibetan anticyclone are simulated closer to the verification analysis by ISRO vegetation. The 2 m air temperature is also better simulated by ISRO vegetation over the northeastern India, showing greater spatial variability over the region. However, the JJAS total rainfall over north India and Deccan coast is better simulated using the USGS vegetation. Sensible heat flux over north-west India is also better simulated by MM5-USGS.     

中国极端气温季节变化对全球变暖减缓的响应分析

利用经过质量控制和均一化处理的中国气象站点1979-2014年逐月最高气温和最低气温资料,对806个无缺测站的数据进行趋势分析和比较,并且计算了各季节对变暖减缓的贡献率,结果表明:中国区域极端气温(最高和最低气温)存在变暖减缓或变冷现象,而不同区域在不同季节对全球变暖减缓的响应程度不同.相比于1979-1999年,2000-2014年极端气温在全国大部分地区春、冬季有明显的变暖减缓或者变冷现象,在长江流域以北大部分地区极端气温在夏季变暖减缓或变冷现象明显,而秋季全国大部分地区最低气温有明显的增暖现象.全国许多地区春季是导致极端气温变暖减缓或变冷的最主要季节,而夏、秋、冬季则是导致部分地区变暖减缓或变冷的主要季节,此外秋季也是导致全国许多地区最低气温变暖的最主要季节.我国大部分地区2000-2014年的变暖减缓或变冷趋势可能受太平洋年代际振荡(PDO)冷位相的调控,而PDO冷位相对最低气温的影响范围更大一些.     

An innovative tailored seasonal rainfall forecasting production in Zimbabwe

Farmers?? adaptation to climate change over southern Africa may become an elusive concept if adequate attention is not rendered to the most important adaptive tool, the regional seasonal forecasting system. Uptake of the convectional seasonal rainfall forecasts issued through the southern African regional climate outlook forum process in Zimbabwe is very low, most probably due to an inherent poor forecast skill and inadequate lead time. Zimbabwe??s recurrent droughts are never in forecast, and the bias towards near normal conditions is almost perpetual. Consequently, the forecasts are poorly valued by the farmers as benefits accrued from these forecasts are minimal. The dissemination process is also very complicated, resulting in the late and distorted reception. The probabilistic nature of the forecast renders it difficult to interpret by the farmers, hence the need to review the whole system. An innovative approach to a regional seasonal forecasting system developed through a participatory process so as to offer a practically possible remedial option is described in this paper. The main added advantage over the convectional forecast is that the new forecast system carries with it, predominantly binary forecast information desperately needed by local farmers??whether a drought will occur in a given season. Hence, the tailored forecast is easier for farmers to understand and act on compared to the conventional method of using tercile probabilities. It does not only provide a better forecasting skill, but gives additional indications of the intra-seasonal distribution of the rainfall including onsets, cessations, wet spell and dry spell locations for specific terciles. The lead time is more than 3?months, which is adequate for the farmers to prepare their land well before the onset of the rains. Its simplicity renders it relatively easy to use, with model inputs only requiring the states of El Nino Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) climate modes. The developed forecast system could be one way to enhance management of risks and opportunities in rain-fed agriculture among small-holder farmers not only in Zimbabwe but also throughout the SADC region where the impact of ENSO and/or IOD on a desired station rainfall is significant.     

Extreme temperature scenarios in Mexicali,Mexico under climate change conditions

Extreme weather events can have severe consequences for the population and the environment. Therefore, in this study a temporal trend of annual temperatures was built with a time series from 1950 to 2010 for Mexicali, Mexico, and estimates of 5- to 100-year return periods are provided by modeling of summer maximum and winter minimum temperatures. A non-parametric Kendall’s tau test and the Sen’s slope estimator were used to compute trends. The generalized extreme value (GEV) distribution was applied to the approximation of block maxima and the generalized Pareto distribution (GPD) to values over a predetermined threshold. Due to the non-stationary characteristic of the series of temperature values, the temporal trend was included as a covariable in the location parameter and substantial improvements were observed, particularly with the extreme minimum temperature, compared to that obtained with the GEV with no covariable and with the GPD. A positive and significant statistically trend in both summer maximum temperature and winter minimum temperature was found. By the end of 21st century the extreme maximum temperature could be 2 to 3 °C higher than current, and the winter could be less severe, as the probabilistic model suggests increases of 7 to 9 °C in the extreme minimum temperature with respect to the base period. The foreseeable consequences on Mexicali city are discussed.     

A Study of the Extended-range Forecast for the Low Frequency Temperature and High Temperature Weather over the Lower Reaches of Yangtze River Valley in Summer

Based on the observational data, the variations of Intraseasonal Oscillation (ISO) of the daily temperatures and its relationships to the high temperature in summer over the lower reaches of the Yangtze River Valley (LYRV) were studied for the period of 1979-2011. It is found that the daily temperatures over LYRV in May-August was mainly of periodic oscillations of 1525, 3060 and 6070 days, and the interannual variation of the intensity of its 3060-day oscillation had a strongly positive correlation with the number of days with daily highest temperature over 35 ℃ in July-August. Low frequency components of daily temperature in the LYRV, and the principal components of the Eastern Asian 850 hPa low frequency temperature, over a time period ranging from 1979 to 2000, were used to establish the Extended Complex Autoregressive model (ECAR) on an extended-range forecast of the 3060-day low frequency temperature over the LYRV. A 11-year independent real-time extended-range forecast was conducted on the extended-range forecast of low frequency component of the temperature over the LYRV in May-August, for the period ranging from 2001 to 2011. These experimental results show that this ECAR model, which is based on a data-driven model, has a good forecast skill at the lead time of approximately 23 days, with a forecast ability superior to the traditional autoregressive (AR) model. Hence, the development and variation of the leading 3060-day modes for the Eastern Asian 850 hPa low frequency temperatures and temporal evolutions of their relationships to low frequency components of the temperature over the LYRV in summer are very helpful in predicting the persistent high temperature over the LYRV at a 20 to 25 days lead.