Improved multisite stochastic weather generation with applications to historical data in South Korea

In this paper, we propose an improved multisite weather generation with applications to the historical data in South Korea. The proposed method improve the algorithm of Wilks (1998, 1999) by automatically selecting an optimal model that represents precipitation amounts and by providing a procedure to obtain a symmetric positive definite estimate for the covariance matrix. The proposed method is computationally fast, and hence, it can be feasible to handle a massive data. We apply the proposed method to the precipitation and temperature data collected 170 stations in South Korea for the period 1976–2005 which are given by the Korea Meteorological Administration (KMA). Results of the proposed method demonstrate the promising performance in terms of spatial correlation and long-term variation as compared with those of the multisite method of Wilks (1998) and the single-site weather generator.     

Stochastic precipitation generator with hidden state covariates

Time series of daily weather such as precipitation, minimum temperature and maximum temperature are commonly required for various fields. Stochastic weather generators constitute one of the techniques to produce synthetic daily weather. The recently introduced approach for stochastic weather generators is based on generalized linear modeling (GLM) with covariates to account for seasonality and teleconnections (e.g., with the El Niño). In general, stochastic weather generators tend to underestimate the observed interannual variance of seasonally aggregated variables. To reduce this overdispersion, we incorporated time series of seasonal dry/wet indicators in the GLM weather generator as covariates. These seasonal time series were local (or global) decodings obtained by a hidden Markov model of seasonal total precipitation and implemented in the weather generator. The proposed method is applied to time series of daily weather from Seoul, Korea and Pergamino, Argentina. This method provides a straightforward translation of the uncertainty of the seasonal forecast to the corresponding conditional daily weather statistics.     

Sensitivity of Ceres-Maize Yields to Statistical Structure of Daily Weather Series

To study impacts of climate variations on cropproduction, the growth models are used to simulateyields in present vs. changed climate conditions.Met&Roll is a four-variate (precipitation amount,solar radiation, minimum and maximum temperatures) stochasticweather generator used to supply synthetic dailyweather series for the crop growth model CERES-Maize.Three groups of experiments were conducted in thisstudy: (1) Validation of Met&Roll reveals some discrepanciesin the statistical structure of synthetic weatherseries, e.g., (i) the frequency of occurrence of longdry spells, extreme values of daily precipitationamount and variability of monthly means areunderestimated by the generator; (ii) correlations andlag-1 correlations among weather characteristicsexhibit a significant annual cycle not assumed by themodel. On the whole, the best fit of the observed andsynthetic weather series is experienced in summermonths. (2) The Wilcoxon test was employed to comparedistributions of maize yields simulated with use ofobserved vs. synthetic weather series. As nostatistically significant differences were detected,it is assumed that the generator imperfections inreproducing the statistical structure of weatherseries negligibly affect the model yields. (3) Thesensitivity of model yields to selectedcharacteristics of the daily weather series wasexamined. Emphasis was placed on the characteristicsnot addressed by typical GCM-based climate changescenarios: daily amplitude of temperature, persistenceof the weather series, shape of the distribution ofdaily precipitation amount, and frequency ofoccurrence of wet days. The results indicate that someof these characteristics may significantly affect cropyields and should therefore be considered in thedevelopment of climate change scenarios.     

The frequency, intensity, and diurnal cycle of precipitation in surface and satellite observations over low- and mid-latitudes

Global precipitation data sets with high spatial and temporal resolution are needed for many applications, but they were unavailable before the recent creation of several such satellite products. Here, we evaluate four different satellite data sets of hourly or 3-hourly precipitation (namely CMORPH, PERSIANN, TRMM 3B42 and a microwave-only product referred to as MI) by comparing the spatial patterns in seasonal mean precipitation amount, daily precipitation frequency and intensity, and the diurnal and semidiurnal cycles among them and with surface synoptic weather reports. We found that these high-resolution products show spatial patterns in seasonal mean precipitation amount comparable to other monthly products for the low- and mid-latitudes, and the mean daily precipitation frequency and intensity maps are similar among these pure satellite-based precipitation data sets and consistent with the frequency derived using weather reports over land. The satellite data show that spatial variations in mean precipitation amount come largely from precipitation frequency rather than intensity, and that the use of satellite infrared (IR) observations to improve sampling does not change the mean frequency, intensity and the diurnal cycle significantly. Consistent with previous studies, the satellite data show that sub-daily variations in precipitation are dominated by the 24-h cycle, which has an afternoon–evening maximum and mean-to-peak amplitude of 30–100% of the daily mean in precipitation amount over most land areas during summer. Over most oceans, the 24-h harmonic has a peak from midnight to early morning with an amplitude of 10–30% during both winter and summer. These diurnal results are broadly consistent with those based on the weather reports, although the time of maximum in the satellite precipitation is a few hours later (especially for TRMM and PERSIANN) than that in the surface observations over most land and ocean, and it is closer to the phase of showery precipitation from the weather reports. The TRMM and PERSIANN precipitation shows a spatially coherent time of maximum around 0300–0600 local solar time (LST) for a weak (amplitude <20%) semi-diurnal (12-h) cycle over most mid- to high-latitudes, comparable to 0400–0600 LST in the surface data. The satellite data also confirm the notion that the diurnal cycle of precipitation amount comes mostly from its frequency rather than its intensity over most low and mid-latitudes, with the intensity has only about half of the strength of the diurnal cycle in the frequency and amount. The results suggest that these relatively new precipitation products can be useful for many applications.     

Evaluation of the weather generator CLIGEN with daily precipitation characteristics in Korea

The ability of the CLImate GENerator (CLIGEN) weather generator to reproduce daily precipitation characteristics for Korea was assessed on the basis of 55-year long historical daily precipitation records from eight weather stations (Seoul, Incheon, Daegu, Ulsan, Gwangju, Busan, Kangneung, and Jeonju) representing different parts of the Korean peninsula. The basic statistics of daily precipitation (mean, standard deviation, skewness of daily precipitation, number of rainy days, and the lengths of wet/dry period), probability distribution characteristics of daily precipitation (percentiles and maximum value), and the spatial covariance statistic generated by CLIGEN were compared with those derived from the observed weather series. Significance tests were conducted on the difference between the historical and generated statistics with the 1% significance level. The results show that CLIGEN simulates most of the daily precipitation characteristics satisfactorily with a tendency to slightly underestimate the mean and variability of daily precipitation. Especially, the number of rainy days is perfectly reproduced with mean relative error of 0.4% across all the stations. It is also found that the spatial covariance statistic from eight different stations is well reproduced by CLIGEN with respect to the leading EOF mode of summer season daily precipitation.     

Evaluation of Unified Model Microphysics in High-resolution NWP Simulations Using Polarimetric Radar Observations

The UK Met Office Unified Model(UM) is employed by many weather forecasting agencies around the globe. This model is designed to run across spatial and time scales and known to produce skillful predictions for large-scale weather systems. However, the model has only recently begun running operationally at horizontal grid spacings of ～1.5 km [e.g.,at the UK Met Office and the Korea Meteorological Administration(KMA)]. As its microphysics scheme was originally designed and tuned for large-scale precipitation systems, we investigate the performance of UM microphysics to determine potential inherent biases or weaknesses. Two rainfall cases from the KMA forecasting system are considered in this study: a Changma(quasi-stationary) front, and Typhoon Sanba(2012). The UM output is compared to polarimetric radar observations in terms of simulated polarimetric radar variables. Results show that the UM generally underpredicts median reflectivity in stratiform rain, producing high reflectivity cores and precipitation gaps between them. This is partially due to the diagnostic rain intercept parameter formulation used in the one-moment microphysics scheme. Model drop size is generally both underand overpredicted compared to observations. UM frozen hydrometeors favor generic ice(crystals and snow) rather than graupel, which is reasonable for Changma and typhoon cases. The model performed best with the typhoon case in terms of simulated precipitation coverage.     

Trends in Precipitation Variability: Prague (The Czech Republic)

Variability in precipitation at scales from 1 to 10 days was investigated with the use of the time series measured at Prague-Sporilov (the Czech Republic) between 1994 and 2001. Variability was detected by the method of absolute difference in precipitation between two adjacent discrete time periods. The results indicated a general increase in precipitation variability at all investigated scales within the 8-yr observational period. The variability patterns also showed quasi-seasonal variations. The summer wetter season proved to be the most variable. The analysis was accomplished by the investigations of precipitation variability at a monthly scale based on a century-long historical time record. On a longer time perspective, precipitation variability exhibits a general increase interrupted by quasi-decadal oscillations. The range of quasi-decadal variability has become more pronounced after about 1950, the fact that hints the possibility of further intensification of the hydrologic cycle. An obtained significant correlation between the North Atlantic Oscillation (NAO) activity and precipitation variability implies that the NAO may account for a large fraction of precipitation variability. Higher NAO-index values tend to be associated with low variability. The variability investigations may have a certain implication for climate change assessments both at the local scales as well as associated with the build-up of greenhouse gases.     

Does El Niño-Southern Oscillation affect the precipitation in Korea on seasonal time scales?

A number of studies in the past two decades have attempted to find the relationship between the precipitation in Korea and the El Niño-Southern Oscillation (ENSO) on various time scales. Comprehensive analyses of station precipitation data in Korea for the 61-year period, 1954-2014, in this study show that the effects of ENSO on the seasonal precipitation in Korea are practically negligible. The correlation between summer precipitation and ENSO is insignificant regardless of the intensity, type (e.g., eastern-Pacific or central-Pacific), and stage (e.g., developing, mature, or decaying) of ENSO. Somewhat meaningful correlation between ENSO and precipitation in Korea occurs only in the ENSO-developing fall. Because summer rainfall accounts for over half of the annual total and fall is a dry season in Korea, the overall effects of ENSO on precipitation in Korea are practically nonexistent.     

A multi-proxy approach for revealing recent climatic changes in the Russian Altai

For the first time we present a multi-proxy data set for the Russian Altai, consisting of Siberian larch tree-ring width (TRW), latewood density (MXD), δ¹³C and δ¹⁸O in cellulose chronologies obtained for the period 1779–2007 and cell wall thickness (CWT) for 1900–2008. All of these parameters agree well between each other in the high-frequency variability, while the low-frequency climate information shows systematic differences. The correlation analysis with temperature and precipitation data from the closest weather station and gridded data revealed that annual TRW, MXD, CWT, and δ¹³C data contain a strong summer temperature signal, while δ¹⁸O in cellulose represents a mixed summer and winter temperature and precipitation signal. The temperature and precipitation reconstructions from the Belukha ice core and Teletskoe lake sediments were used to investigate the correspondence of different independent proxies. Low frequency patterns in TRW and δ¹³C chronologies are consistent with temperature reconstructions from nearby Belukha ice core and Teletskoe lake sediments showing a pronounced warming trend in the last century. Their combination could be used for the regional temperature reconstruction. The long-term δ¹⁸O trend agrees with the precipitation reconstruction from the Teletskoe lake sediment indicating more humid conditions during the twentieth century. Therefore, these two proxies could be combined for the precipitation reconstruction.     

High-Frequency and Low-Frequency Variability in Stochastic Daily Weather Generator and Its Effect on Agricultural and Hydrologic Modelling

The high-frequency and low-frequency variabilities, which are often misreproduced by the daily weather generators, have a significant effect on modelling weather-dependent processes. Three modifications are suggested to improve the reproduction of the both variabilities in a four-variate daily weather generator Met&Roll: (i) inclusion of the annual cycle of lag-0 and lag-1 correlations among solar radiation, maximum temperature and minimum temperature, (ii) use of the 3rd order Markov chain to model precipitation occurrence, (iii) applying the monthly generator (based on a first-order autoregressive model) to fit the low-frequency variability. The tests are made to examine the effects of the three new features on (i) a stochastic structure of the synthetic series, and on (ii) outputs from CERES-Wheat crop model (crop yields) and SAC-SMA rainfall-runoff model (monthly streamflow characteristics, distribution of 5-day streamflow) fed by the synthetic weather series. The results are compared with those obtained with the observed weather series.Results: (i) The inclusion of the annual cycle of the correlations has rather ambiguous effect on the temporal structure of the weather characteristics simulated by the generator and only insignificant effect on the output from either simulation model. (ii) Increased order of the Markov chain improves modelling of precipitation occurrence series (especially long dry spells), and correspondingly improves reliability of the output from either simulation model. (iii) Conditioning the daily generator on monthly generator has the most positive effect, especially on the output from the hydrological model: Variability of the monthly streamflow characteristics and the frequency of extreme streamflows are better simulated. (iv) Of the two simulation models, the improvements related to the three modifications are more pronounced in the hydrological simulations. This may be also due to the fact that the crop growth simulations were less affected by the imperfections of the unmodified version of Met&Roll.     

A new framework for multi-site weather generator: a two-stage model combining a parametric method with a distribution-free shuffle procedure

Keeping the spatial correlation of synthetic precipitation data is of utmost importance for hydrological modeling; however, most present weather generators are single-site models and ignore the spatial dependence in daily weather data. Multi-site weather generator is an effective method to solve this problem. This study proposes a new framework for multi-site weather generator denoted as two-stage weather generator (TSWG), in which the first stage generates the single-site precipitation occurrence and amount with a parametric chain-dependent process, and the second stage rebuilds the spatial correlation of the synthetic data using a post-processing, distribution-free shuffle procedure. Results show that TSWG reproduces the statistical parameters of the parametric stage quite well, such as wet days and precipitation amount, and it almost perfectly preserves the inter-station correlations of precipitation occurrence and amount as well as their dependences. Most important, it matches the input requirement of hydrological model and gives satisfactory hydrological simulations. There are several advantages for this new framework: (1) only one correlation matrix and two simple steps, no more input variables or iterative optimizations, are needed to rebuild the spatial correlation; (2) the statistical parameters of the observed data can be easily preserved; (3) the inter-station correlations can be satisfactorily rebuilt. As a post-processing method, the shuffle procedure used to reconstruct the spatial correlation has some potential extensions, such as turning current single-site weather generator into multi-site models and generating future multi-site climate scenarios.     

Study of method for synthetic precipitation data for ungauged sites using quantitative precipitation model

A method was developed to estimate a synthetic precipitation record for ungauged sites using irregular coarse observations. The proposed synthetic precipitation data were produced with ultrahigh hourly resolution on a regular 1 × 1 km grid. The proposed method was used to analyze selected real-time observational data collected in South Korea from 2010 to the end of 2014. The observed precipitation data were measured using the Automatic Weather System and Automated Synoptic Observing System. The principal objective of the proposed method was to estimate the additional effects of orography on precipitation introduced by ultrahigh- resolution (1 × 1 km) topography provided by a digital elevation model. The Global Forecast System analysis of the National Centers for Environmental Prediction was used for the upper-atmospheric conditions, necessary for estimating the orographic effects. Precipitation data from 48 of the more than 600 observation sites used in the study, which matched the grid points of the synthetic data, were not included in the synthetic data estimation. Instead, these data were used to evaluate the proposed method by direct comparison with the real observations at these sites. A bias score was investigated by comparison of the synthetic precipitation data with the observations. In this comparison, the number of Hit, False, Miss, and Correct results for 2010-2014 was 74738, 25778, 7544, and 367981, respectively. In the Hit cases, the bias score was 1.22 and the correlation coefficient was 0.74. The means of the differences between the synthetic data and the observations were 0.3, -3.9, -14.4, and -34.9 mm h^-1 and the root mean square errors (RMSEs) were 2.7, 8.3, 19.3, and 39.6 mm h^-1 for the categories of 0.5-10.0, 10.0-30.0, 30.0-50.0, and 50.0-100.0 mm h^-1, respectively. In addition, in each range, the 60% difference between the synthetic precipitation data and the observation data was -1.5 to +1.5, -5.0 to +5.0, -17.0 to +17.0, and -33.0 to +33.0 mm h^-1, respectively. Overall, the correlation coefficient of the synthetic precipitation data was > 0.7 for 43 of the 48 test stations and the RMSE was < 4 mm h^-1 at 31 stations. The results are significant at all evaluation stations at the 0.05 significance level.     

基于EEMD的黄河中上游夏季降水预报方法的研究

传统的统计方法难以很好的对气候系统这一集非线性、非平稳性为一身的多层次系统进行处理。因此集层次化处理和平稳化处理的集合正交经验模态分解技术(EEMD)的提出,为解决上述问题提供了有效的途径。本文选取黄河中上游24个气象观测站的逐月降水资料,结合组合预报和集合预报思路,基于EEMD建立了统计预报模型。其中对降水序列中的高频部分进行了二次平稳化处理,实现对2008—2013年6—8月的降水预报,并用预报评分检测预报效果。结果表明:EEMD模型对黄河中上游夏季降水有着较强的预报能力,在该区域与气候模式和传统的统计方法相比具有更高的精度和更好的应用前景。     

A millennial long March–July precipitation reconstruction for southern-central England

We present a millennial long dendroclimatic reconstruction of spring/summer precipitation for southern-central England. Previous research identified a significant moisture stress signal in ring-width data measured from oak trees growing in southern England. In this study, we build upon this earlier work, specifically targeting south-central England, to derive a well replicated oak ring-width composite chronology using both living and historical material. The data-set includes 352 living trees (AD 1629–2009) and 1540 individual historical series (AD 663–1925). The period expressed by at least 50 trees in any year is AD 980–2009. Calibration experiments identify the optimal seasonal predictand target as March–July precipitation (1901–2007: r² = 0.33). However, comparison with the long Kew Gardens precipitation record indicates a weakening in tree-growth/climate response from ~1800 to 1920 which we speculate may be related to smoke and sulphur dioxide (SO₂) emissions at that time which may have also contributed to a decrease in tree productivity. The time-series derived using the regional curve standardisation method to capture lower frequency information shows a mediaeval period with alternating multi-decade-long dry and wet periods, with AD 1153–1172 being the wettest reconstructed 20-year period in the whole record. Drier conditions are prevalent from ~1300 to the early sixteenth century followed by a period of increasing precipitation levels. The most recent four centuries of the record appear similar to the mediaeval period with multiple decade-long dry and wet periods. The late twentieth century is the second reconstructed wettest period. These centennial hydroclimatic trends are in broad agreement with independent regional scale hydroclimatic reconstructions from tree-ring (East Anglia), historical, speleothem and peat water level proxy archives in the United Kingdom and appear coupled with reconstructed sea surface temperature changes in the North Atlantic which in turn influence the Atlantic meridional overturning circulation and westerly airflow across the UK.     

Development of climate change projections for small watersheds using multi-model ensemble simulation and stochastic weather generation

Regional climate models (RCMs) have been increasingly used for climate change studies at the watershed scale. However, their performance is strongly dependent upon their driving conditions, internal parameterizations and domain configurations. Also, the spatial resolution of RCMs often exceeds the scales of small watersheds. This study developed a two-step downscaling method to generate climate change projections for small watersheds through combining a weighted multi-RCM ensemble and a stochastic weather generator. The ensemble was built on a set of five model performance metrics and generated regional patterns of climate change as monthly shift terms. The stochastic weather generator then incorporated these shift terms into observed climate normals and produced synthetic future weather series at the watershed scale. This method was applied to the Assiniboia area in southern Saskatchewan, Canada. The ensemble led to reduced biases in temperature and precipitation projections through properly emphasizing models with good performance. Projection of precipitation occurrence was particularly improved through introducing a weight-based probability threshold. The ensemble-derived climate change scenario was well reproduced as local daily weather series by the stochastic weather generator. The proposed combination of dynamical downscaling and statistical downscaling can improve the reliability and resolution of future climate projection for small prairie watersheds. It is also an efficient solution to produce alternative series of daily weather conditions that are important inputs for examining watershed responses to climate change and associated uncertainties.     

华北地区夏季降水日变化的时空分布特征

利用2008~2014年间全国自动站观测降水和CMORPH[CPC（Climate Prediction Center）morphing technique]卫星反演降水资料融合而成的0.1°×0.1°小时降水产品揭示了华北夏季降水的日变化特征,发现华北多数地区夏季降水量和降水频率日变化呈现出明显的双峰特征且存在明显的区域性差异。在太行山以西地区,降水量和降水频率的日峰值出现在傍晚18:00左右（北京时）,规律性最强;而在太行山以东的平原和沿海地区,日峰值一般出现在上午。研究不同持续时间降水对总降水的贡献发现短时降水对傍晚的降水日峰值贡献较大,而长时降水则对凌晨的峰值影响更大。分析不同强度降水对总降水量的贡献结果表明,0.1~10 mm h-1强度降水较其它强度降水对夏季华北地区总降水量贡献更大,随着降水强度的增加降水量日变化的峰值个数增加。     

低频天气图方法在四川盆地夏季延伸期强降水预报中的应用

利用四川盆地2008~2013年夏季降水量资料以及500hPa风场资料,基于低频天气图方法,通过分析影响四川盆地强降水过程的500h Pa低频天气系统的活动规律,对强降水时段对应的低频风序列做经验正交分解,分析低频系统的主要空间分布型及其流场配置。统计降水时段对应的低频系统空间位置、分布及持续出现频数,划分出与预测区域降水过程密切关联的8个低频关键区,主要包括中低纬的西太平洋副热带高压及台风的主要活动区(1)、南海附近地区(2)、阿拉伯海和孟加拉湾附近地区(3),中高纬的蒙古至河套附近地区(5)和青藏高原及其以北的低频系统集中区域(6)这五个活跃关键区。计算各关键区低频系统历史周期发现低纬关键区(1、2、3)以及高原关键区(6)低频系统的周期都较短,而中高纬关键区(4、5、7、8)低频系统的周期相对较长。建立预测区域强降水过程的低频图预测模型。用此方法对2013年四川盆地延伸期强降水过程进行预报试验,发现预测6~7月上中旬的强降水过程效果较好,但对盛夏高温连晴伏旱时段(7月下旬8月)的预报能力有所下降。      

Effects of prescribed initial cloud droplet spectra on convective cloud and precipitation developments under different thermodynamic conditions: A modeling and observational study

A two-dimensional cloud model with bin microphysics was used to investigate the effects of cloud condensation nuclei (CCN) concentrations and thermodynamic conditions on convective cloud and precipitation developments. Two different initial cloud droplet spectra were prescribed based on the total CCN concentrations of maritime (300 cm^− 3) and continental (1000 cm^− 3) air masses, and the model was run on eight thermodynamic conditions obtained from observational soundings. Six-hourly sounding data and 1-hourly precipitation data from two nearby weather stations in Korea were analyzed for the year 2002 to provide some observational support for the model results.For one small Convective Available Potential Energy (CAPE) ( 300 J kg^− 1) sounding, the maritime and continental differences were incomparably large. The crucial difference was the production of ice phase hydrometeors in the maritime cloud and only water drops in the continental cloud. Ice phase hydrometeors and intrinsically large cloud drops of the maritime cloud eventually lead to significant precipitation. Meanwhile negligible precipitation developed from the continental cloud. For the three other small CAPE soundings, generally weak convective clouds developed but the maritime and continental clouds were of the same phases (both warm or both cold) and their differences were relatively small.Model runs with the four large CAPE ( 3000 J kg^− 1) soundings demonstrated that the depth between the freezing level (FL) and the lifting condensation level (LCL) was crucial to determine whether a cloud becomes a cold cloud or not, which in turn was found to be a crucial factor to enhance cloud invigoration with the additional supply of freezing latent heat. For two large CAPE soundings, FL–LCL was so deep that penetration of FL was prohibitive, and precipitation was only mild in the maritime clouds and negligible in the continental clouds. Two other soundings of similarly large CAPE had small FL–LCL, and both the maritime and continental clouds became cold clouds. Precipitation was strong for both but much more so in the maritime clouds, while the maximum updraft velocity and the cloud top were slightly higher in continental clouds. Although limited to small CAPE cases, more precipitation for smaller FL–LCL for a selected group of precipitation and thermodynamic sounding data from Korea was in support of these model results in its tendency.These results clearly demonstrated that the CCN effects on cloud and precipitation developments critically depended on the given thermodynamic conditions and not just the CAPE but the entire structure of the thermodynamic profiles had to be taken into account.     

松花江、嫩江流域1998年夏季暴雨过程天气分析

利用松花江、嫩江流域内９４个气象台站的观测资料和ＮＣＡＲ／ＮＣＥＰ再分析资料,分析了１９９８年夏季降水集中期和主要暴雨过程发生时的天气形势特点。之所以会连续出现暴雨、大暴雨,归纳起来有以下天气特点：（１）亚洲中高纬度阻塞形势稳定;（２）长时间受东北冷涡控制;（３）西太平洋副热带高压短时间北进,位置适中;（４）盛夏北方季风较强盛。     

Future trend of extreme value distributions of wintertime surface air temperatures over Korea and the associated physical changes

Daily winter temperatures in Korea have been analyzed via CSEOF analysis. Then, each PC time series was detrended and was fitted to an AR (autoregressive) model. Based on the identified AR model, an artificial time series of arbitrary length can be generated by using an arbitrary white-noise time series. In this way, one hundred new sets of PC time series were generated over the period of 1973–2058. Then, the trend for each PC time series was added back to the artificial PC time series extending the trend until 2058. Ultimately, artificial daily winter temperatures in Korea have been constructed by using the artificial PC time series and the original loading vectors derived from the observational data. The 100 new data sets have been investigated in order to understand the winter temperature variability 50 years into the future. Regression analysis in CSEOF space shows that temperature increase in Korea is associated with increased 850-hPa air temperature over most of the Asian domain (97°-153°E × 22°-73°N) and increased 850-hPa geopotential height in the southern part of the domain. As a result, southerly and southeasterly wind anomalies develop carrying positive temperature anomalies northward and northwestward. Both the 200-hPa air temperature and geopotential height changes indicate that there will be fairly significant northward shift of the jet stream in future. The standard deviation of the 200-hPa potential vorticity increases implying that shortwave trough and henceforth baroclinic instability will increase in future. Finally, GEV (Generalized Extreme Value) distribution and GPD (Generalized Pareto distribution) distribution have been compared between the observational records and the future records of the same length. The extreme value distributions based on the synthetic datasets show that warm extreme events will be more extreme in future and cold extreme events, on the other hand, will be less extreme. This study provides an estimate of future temperatures based on the observational data and serves as an independent baseline solution for comparisons with numerical model solutions.