Uncertainty analysis of statistical downscaling models using general circulation model over an international wetland

Regression-based statistical downscaling model (SDSM) is an appropriate method which broadly uses to resolve the coarse spatial resolution of general circulation models (GCMs). Nevertheless, the assessment of uncertainty propagation linked with climatic variables is essential to any climate change impact study. This study presents a procedure to characterize uncertainty analysis of two GCM models link with Long Ashton Research Station Weather Generator (LARS-WG) and SDSM in one of the most vulnerable international wetland, namely “Shadegan” in an arid region of Southwest Iran. In the case of daily temperature, uncertainty is estimated by comparing monthly mean and variance of downscaled and observed daily data at a 95 % confidence level. Uncertainties were then evaluated from comparing monthly mean dry and wet spell lengths and their 95 % CI in daily precipitation downscaling using 1987–2005 interval. The uncertainty results indicated that the LARS-WG is the most proficient model at reproducing various statistical characteristics of observed data at a 95 % uncertainty bounds while the SDSM model is the least capable in this respect. The results indicated a sequences uncertainty analysis at three different climate stations and produce significantly different climate change responses at 95 % CI. Finally the range of plausible climate change projections suggested a need for the decision makers to augment their long-term wetland management plans to reduce its vulnerability to climate change impacts.     

Application of physical scaling towards downscaling climate model precipitation data

Physical scaling (SP) method downscales climate model data to local or regional scales taking into consideration physical characteristics of the area under analysis. In this study, multiple SP method based models are tested for their effectiveness towards downscaling North American regional reanalysis (NARR) daily precipitation data. Model performance is compared with two state-of-the-art downscaling methods: statistical downscaling model (SDSM) and generalized linear modeling (GLM). The downscaled precipitation is evaluated with reference to recorded precipitation at 57 gauging stations located within the study region. The spatial and temporal robustness of the downscaling methods is evaluated using seven precipitation based indices. Results indicate that SP method-based models perform best in downscaling precipitation followed by GLM, followed by the SDSM model. Best performing models are thereafter used to downscale future precipitations made by three global circulation models (GCMs) following two emission scenarios: representative concentration pathway (RCP) 2.6 and RCP 8.5 over the twenty-first century. The downscaled future precipitation projections indicate an increase in mean and maximum precipitation intensity as well as a decrease in the total number of dry days. Further an increase in the frequency of short (1-day), moderately long (2–4 day), and long (more than 5-day) precipitation events is projected.     

Downscaling daily precipitation over the Yellow River source region in China: a comparison of three statistical downscaling methods

Three statistical downscaling methods are compared with regard to their ability to downscale summer (June–September) daily precipitation at a network of 14 stations over the Yellow River source region from the NCEP/NCAR reanalysis data with the aim of constructing high-resolution regional precipitation scenarios for impact studies. The methods used are the Statistical Downscaling Model (SDSM), the Generalized LInear Model for daily CLIMate (GLIMCLIM), and the non-homogeneous Hidden Markov Model (NHMM). The methods are compared in terms of several statistics including spatial dependence, wet- and dry spell length distributions and inter-annual variability. In comparison with other two models, NHMM shows better performance in reproducing the spatial correlation structure, inter-annual variability and magnitude of the observed precipitation. However, it shows difficulty in reproducing observed wet- and dry spell length distributions at some stations. SDSM and GLIMCLIM showed better performance in reproducing the temporal dependence than NHMM. These models are also applied to derive future scenarios for six precipitation indices for the period 2046–2065 using the predictors from two global climate models (GCMs; CGCM3 and ECHAM5) under the IPCC SRES A2, A1B and B1scenarios. There is a strong consensus among two GCMs, three downscaling methods and three emission scenarios in the precipitation change signal. Under the future climate scenarios considered, all parts of the study region would experience increases in rainfall totals and extremes that are statistically significant at most stations. The magnitude of the projected changes is more intense for the SDSM than for other two models, which indicates that climate projection based on results from only one downscaling method should be interpreted with caution. The increase in the magnitude of rainfall totals and extremes is also accompanied by an increase in their inter-annual variability.     

Evaluation of different downscaling techniques for hydrological climate-change impact studies at the catchment scale

Hydrological modeling for climate-change impact assessment implies using meteorological variables simulated by global climate models (GCMs). Due to mismatching scales, coarse-resolution GCM output cannot be used directly for hydrological impact studies but rather needs to be downscaled. In this study, we investigated the variability of seasonal streamflow and flood-peak projections caused by the use of three statistical approaches to downscale precipitation from two GCMs for a meso-scale catchment in southeastern Sweden: (1) an analog method (AM), (2) a multi-objective fuzzy-rule-based classification (MOFRBC) and (3) the Statistical DownScaling Model (SDSM). The obtained higher-resolution precipitation values were then used to simulate daily streamflow for a control period (1961–1990) and for two future emission scenarios (2071–2100) with the precipitation-streamflow model HBV. The choice of downscaled precipitation time series had a major impact on the streamflow simulations, which was directly related to the ability of the downscaling approaches to reproduce observed precipitation. Although SDSM was considered to be most suitable for downscaling precipitation in the studied river basin, we highlighted the importance of an ensemble approach. The climate and streamflow change signals indicated that the current flow regime with a snowmelt-driven spring flood in April will likely change to a flow regime that is rather dominated by large winter streamflows. Spring flood events are expected to decrease considerably and occur earlier, whereas autumn flood peaks are projected to increase slightly. The simulations demonstrated that projections of future streamflow regimes are highly variable and can even partly point towards different directions.     

Simulation of extreme precipitation indices in the Yangtze River basin by using statistical downscaling method (SDSM)

In this study, the applicability of the statistical downscaling model (SDSM) in modeling five extreme precipitation indices including R10 (no. of days with precipitation ≥10?mm?day^?1), SDI (simple daily intensity), CDD (maximum number of consecutive dry days), R1d (maximum 1-day precipitation total) and R5d (maximum 5-day precipitation total) in the Yangtze River basin, China was investigated. The investigation mainly includes the calibration and validation of SDSM model on downscaling daily precipitation, the validation of modeling extreme precipitation indices using independent period of the NCEP reanalysis data, and the projection of future regional scenarios of extreme precipitation indices. The results showed that: (1) there existed good relationship between the observed and simulated extreme precipitation indices during validation period of 1991–2000, the amount and the change pattern of extreme precipitation indices could be reasonably simulated by SDSM. (2) Under both scenarios A2 and B2, during the projection period of 2010–2099, the changes of annual mean extreme precipitation indices in the Yangtze River basin would be not obvious in 2020s; while slightly increase in the 2050s; and significant increase in the 2080s as compared to the mean values of the base period. The summer might be the more distinct season with more projected increase of each extreme precipitation indices than in other seasons. And (3) there would be distinctive spatial distribution differences for the change of annual mean extreme precipitation indices in the river basin, but the most of Yangtze River basin would be dominated by the increasing trend.     

Uncertainty analysis of statistical downscaling models using Hadley Centre Coupled Model

Regression-based statistical downscaling is a method broadly used to resolve the coarse spatial resolution of general circulation models. Nevertheless, the assessment of uncertainties linked with climatic variables is essential to climate impact studies. This study presents a procedure to characterize the uncertainty in regression-based statistical downscaling of daily precipitation and temperature over a highly vulnerable area (semiarid catchment) in the west of Iran, based on two downscaling models: a statistical downscaling model (SDSM) and an artificial neural network (ANN) model. Biases in mean, variance, and wet/dry spells are estimated for downscaled data using vigorous statistical tests for 30 years of observed and downscaled daily precipitation and temperature data taken from the National Center for Environmental Prediction reanalysis predictors for the years of 1961 to 1990. In the case of daily temperature, uncertainty is estimated by comparing monthly mean and variance of downscaled and observed daily data at a 95 % confidence level. In daily precipitation, downscaling uncertainties were evaluated from comparing monthly mean dry and wet spell lengths and their confidence intervals, cumulative frequency distributions of monthly mean of daily precipitation, and the distributions of monthly wet and dry days for observed and modeled daily precipitation. Results showed that uncertainty in downscaled precipitation is high, but simulation of daily temperature can reproduce extreme events accurately. Finally, this study shows that the SDSM is the most proficient model at reproducing various statistical characteristics of observed data at a 95 % confidence level, while the ANN model is the least capable in this respect. This study attempts to test uncertainties of regression-based statistical downscaling techniques in a semiarid area and therefore contributes to an improvement of the quality of predictions of climate change impact assessment in regions of this type.     

Statistical downscaling of daily mean temperature, pan evaporation and precipitation for climate change scenarios in Haihe River, China

A statistical downscaling method (SDSM) was evaluated by simultaneously downscaling air temperature, evaporation, and precipitation in Haihe River basin, China. The data used for evaluation were large-scale atmospheric data encompassing daily NCEP/NCAR reanalysis data and the daily mean climate model results for scenarios A2 and B2 of the HadCM3 model. Selected as climate variables for downscaling were measured daily mean air temperature, pan evaporation, and precipitation data (1961–2000) from 11 weather stations in the Haihe River basin. The results obtained from SDSM showed that: (1) the pattern of change in and numerical values of the climate variables can be reasonably simulated, with the coefficients of determination between observed and downscaled mean temperature, pan evaporation, and precipitation being 99%, 93%, and 73%, respectively; (2) systematic errors existed in simulating extreme events, but the results were acceptable for practical applications; and (3) the mean air temperature would increase by about 0.7°C during 2011~2040; the total annual precipitation would decrease by about 7% in A2 scenario but increase by about 4% in B2 scenario; and there were no apparent changes in pan evaporation. It was concluded that in the next 30 years, climate would be warmer and drier, extreme events could be more intense, and autumn might be the most distinct season among all the changes.     

Prediction of temperature and precipitation in Sudan and South Sudan by using LARS-WG in future

Global warming has brought great pressure on the environment and livelihood conditions in Sudan and South Sudan. It is desirable to analyze and predict the change of critical climatic variables, such as temperature and precipitation, which will provide valuable reference results for future water resources planning and management in the region. The aims of this study are to test the applicability of the Long Ashton Research Station Weather Generator (LARS-WG) model in downscaling daily precipitation and daily maximum (Tmax) and daily minimum (Tmin) temperatures in Sudan and South Sudan and use it to predict future changes of precipitation; Tmin and Tmax for nine stations in Sudan and South Sudan are based on the SRA2 scenario of seven General Circulation Models (GCMs) outputs for the periods of 2011–2030, 2046–2065, and 2080–2099. The results showed that (1) the LARS-WG model produces good performance in downscaling daily precipitation and excellent performance in downscaling Tmax and Tmin in the study region; (2) downscaled precipitation from the prediction of seven GCMs showed great inconsistency in these two regions, which illustrates the great uncertainty in GCMs' results in the regions; (3) predicted precipitation in rainy season JJA (June, July, and August) based on the ensemble mean of seven GCMs showed a decreasing trend in the periods of 2011–2030, 2046–2065, and 2080–2099 in Sudan; however, an increasing trend can be found in SON (September, October, and November) in the future; (4) precipitation in South Sudan has an increasing trend in most seasons in the future except in MAM (March, April, and May) season in 2011–2030; and (5) predictions from seven GCMs showed a similar and continuous increasing trend for Tmax and Tmin in all three future periods, which will bring severe negative influence on improving livelihoods and reducing poverty in Sudan and South Sudan.     

Reduction of systematic biases in regional climate downscaling through ensemble forcing

Simulations of the East Asian summer monsoon for the period of 1979–2001 were carried out using the Weather Research and Forecast (WRF) model forced by three reanalysis datasets (NCEP-R2, ERA-40, and JRA-25). The experiments forced by different reanalysis data exhibited remarkable differences, primarily caused by uncertainties in the lateral boundary (LB) moisture fluxes over the Bay of Bengal and the Philippine Sea. The climatological mean water vapor convergence into the model domain computed from ERA-40 was about 24% higher than that from the NCEP-R2 reanalysis. We demonstrate that using the ensemble mean of NCEP-R2, ERA-40, and JRA-25 as LB forcing considerably reduced the biases in the model simulation. The use of ensemble forcing improved the performance in simulated mean circulation and precipitation, inter-annual variation in seasonal precipitation, and daily precipitation. The model simulated precipitation was superior to that in the reanalysis in both climatology and year-to-year variations, indicating the added value of dynamic downscaling. The results suggest that models having better performance under one set of LB forcing might worsen when another set of reanalysis data is used as LB forcing. Use of ensemble mean LB forcing for assessing regional climate model performance is recommended.     

Assessment of a stochastic downscaling methodology in generating an ensemble of hourly future climate time series

This study extends a stochastic downscaling methodology to generation of an ensemble of hourly time series of meteorological variables that express possible future climate conditions at a point-scale. The stochastic downscaling uses general circulation model (GCM) realizations and an hourly weather generator, the Advanced WEather GENerator (AWE-GEN). Marginal distributions of factors of change are computed for several climate statistics using a Bayesian methodology that can weight GCM realizations based on the model relative performance with respect to a historical climate and a degree of disagreement in projecting future conditions. A Monte Carlo technique is used to sample the factors of change from their respective marginal distributions. As a comparison with traditional approaches, factors of change are also estimated by averaging GCM realizations. With either approach, the derived factors of change are applied to the climate statistics inferred from historical observations to re-evaluate parameters of the weather generator. The re-parameterized generator yields hourly time series of meteorological variables that can be considered to be representative of future climate conditions. In this study, the time series are generated in an ensemble mode to fully reflect the uncertainty of GCM projections, climate stochasticity, as well as uncertainties of the downscaling procedure. Applications of the methodology in reproducing future climate conditions for the periods of 2000–2009, 2046–2065 and 2081–2100, using the period of 1962–1992 as the historical baseline are discussed for the location of Firenze (Italy). The inferences of the methodology for the period of 2000–2009 are tested against observations to assess reliability of the stochastic downscaling procedure in reproducing statistics of meteorological variables at different time scales.     

Comparison of regional climate model and statistical downscaling simulations of different winter precipitation change scenarios over Romania

Summary Regional climate model and statistical downscaling procedures are used to generate winter precipitation changes over Romania for the period 2071–2100 (compared to 1961–1990), under the IPCC A2 and B2 emission scenarios. For this purpose, the ICTP regional climate model RegCM is nested within the Hadley Centre global atmospheric model HadAM3H. The statistical downscaling method is based on the use of canonical correlation analysis (CCA) to construct climate change scenarios for winter precipitation over Romania from two predictors, sea level pressure and specific humidity (either used individually or together). A technique to select the most skillful model separately for each station is proposed to optimise the statistical downscaling signal. Climate fields from the A2 and B2 scenario simulations with the HadAM3H and RegCM models are used as input to the statistical downscaling model. First, the capability of the climate models to reproduce the observed link between winter precipitation over Romania and atmospheric circulation at the European scale is analysed, showing that the RegCM is more accurate than HadAM3H in the simulation of Romanian precipitation variability and its connection with large-scale circulations. Both models overestimate winter precipitation in the eastern regions of Romania due to an overestimation of the intensity and frequency of cyclonic systems over Europe. Climate changes derived directly from the RegCM and HadAM3H show an increase of precipitation during the 2071–2100 period compared to 1961–1990, especially over northwest and northeast Romania. Similar climate change patterns are obtained through the statistical downscaling method when the technique of optimum model selected separately for each station is used. This adds confidence to the simulated climate change signal over this region. The uncertainty of results is higher for the eastern and southeastern regions of Romania due to the lower HadAM3H and RegCM performance in simulating winter precipitation variability there as well as the reduced skill of the statistical downscaling model.     

Backcasting long-term climate data: evaluation of hypothesis

Most often than not, incomplete datasets or short-term recorded data in vast regions impedes reliable climate and water studies. Various methods, such as simple correlation with stations having long-term time series, are practiced to infill or extend the period of observation at stations with missing or short-term data. In the current paper and for the first time, the hypothesis on the feasibility of extending the downscaling concept to backcast local observation records using large-scale atmospheric predictors is examined. Backcasting is coined here to contrast forecasting/projection; the former is implied to reconstruct in the past, while the latter represents projection in the future. To assess our hypotheses, daily and monthly statistical downscaling models were employed to reconstruct past precipitation data and lengthen the data period. Urmia and Tabriz synoptic stations, located in northwestern Iran, constituted two case study stations. SDSM and data-mining downscaling model (DMDM) daily as well as the group method of data handling (GMDH) and model tree (Mp5) monthly downscaling models were trained with National Center for Environmental Prediction (NCEP) data. After training, reconstructed precipitation data of the past was validated against observed data. Then, the data was fully extended to the 1948 to 2009 period corresponding to available NCEP data period. The results showed that DMDM performed superior in generation of monthly average precipitation compared with the SDSM, Mp5, and GMDH models, although none of the models could preserve the monthly variance. This overall confirms practical value of the proposed approach in extension of the past historic data, particularly for long-term climatological and water budget studies.     

Improve the prediction of summer precipitation in the Southeastern China by a hybrid statistical downscaling model

We attempt to apply year-to-year increment prediction to develop an effective statistical downscaling scheme for summer (JJA, June–July–August) rainfall prediction at the station-to-station scale in Southeastern China (SEC). The year-to-year increment in a variable was defined as the difference between the current year and the previous year. This difference is related to the quasi-biennial oscillation in interannual variations in precipitation. Three predictors from observations and six from three general circulation models (GCMs) outputs of the development of a European multi-model ensemble system for seasonal to interannual prediction (DEMETER) project were used to establish this downscaling model. The independent sample test and the cross-validation test show that the downscaling scheme yields better predicted skill for summer precipitation at most stations over SEC than the original DEMETER GCM outputs, with greater temporal correlation coefficients and spatial anomaly correlation coefficients, as well as lower root-mean-square errors.     

山东夏季降水异常的环流特征及降尺度解释应用预测方法研究

基于山东省123个国家级气象观测站1961—2015年夏季降水资料、1991—2015年NCEP再分析资料,分析了山东夏季降水变化特征及其与大气环流的关系。结果表明,在山东夏季降水偏多（少）时,乌拉尔山阻塞高压偏弱（强）,副热带高压偏强（弱）,南亚高压偏强（弱）,并且200 hPa经向风场有明显的类似丝绸之路遥相关型的波列结构。利用国家气候中心第二代海气耦合模式3月起报的未来夏季海平面气压场建立降尺度预测模型,该模型对山东夏季降水的预测符号一致率达到64%,有一定的预测能力,进一步分析发现,模式对关键区环流因子的模拟预测能力显著影响降尺度预测方法的准确率。     

Mediterranean climate extremes in synoptic downscaling assessments

The behaviour of precipitation and maximum temperature extremes in the Mediterranean area under climate change conditions is analysed in the present study. In this context, the ability of synoptic downscaling techniques in combination with extreme value statistics for dealing with extremes is investigated. Analyses are based upon a set of long-term station time series in the whole Mediterranean area. At first, a station-specific ensemble approach for model validation was developed which includes (1) the downscaling of daily precipitation and maximum temperature values from the large-scale atmospheric circulation via analogue method and (2) the fitting of extremes by generalized Pareto distribution (GPD). Model uncertainties are quantified as confidence intervals derived from the ensemble distributions of GPD-related return values and described by a new metric called “ratio of overlapping”. Model performance for extreme precipitation is highest in winter, whereas the best models for maximum temperature extremes are set up in autumn. Valid models are applied to a 30-year period at the end of the twenty-first century (2070–2099) by means of ECHAM5/MPI-OM general circulation model data for IPCC SRES B1 scenario. The most distinctive future changes are observed in autumn in terms of a strong reduction of precipitation extremes in Northwest Iberia and the Northern Central Mediterranean area as well as a simultaneous distinct increase of maximum temperature extremes in Southwestern Iberia and the Central and Southeastern Mediterranean regions. These signals are checked for changes in the underlying dynamical processes using extreme-related circulation classifications. The most important finding connected to future changes of precipitation extremes in the Northwestern Mediterranean area is a reduction of southerly displaced deep North Atlantic cyclones in 2070–2099 as associated with a strengthened North Atlantic Oscillation. Thus, the here estimated future changes of extreme precipitation are in line with the discourse about the influence of North Atlantic circulation variability on the changing climate in Europe.     

A comparison of downscaling techniques in the projection of local climate change and wheat yields

This study aims to evaluate the performance of two mainstream downscaling techniques: statistical and dynamical downscaling and to compare the differences in their projection of future climate change and the resultant impact on wheat crop yields for three locations across New South Wales, Australia. Bureau of Meteorology statistically- and CSIRO dynamically-downscaled climate, derived or driven by the CSIRO Mk 3.5 coupled general circulation model, were firstly evaluated against observed climate data for the period 1980–1999. Future climate projections derived from the two downscaling approaches for the period centred on 2055 were then compared. A stochastic weather generator, LARS-WG, was used in this study to derive monthly climate changes and to construct climate change scenarios. The Agricultural Production System sIMulator-Wheat model was then combined with the constructed climate change scenarios to quantify the impact of climate change on wheat grain yield. Statistical results show that (1) in terms of reproducing the past climate, statistical downscaling performed better over dynamical downscaling in most of the cases including climate variables, their mean, variance and distribution, and study locations, (2) there is significant difference between the two downscaling techniques in projected future climate change except the mean value of rainfall across the three locations for most of the months; and (3) there is significant difference in projected wheat grain yields between the two downscaling techniques at two of the three locations.     

21世纪黄河流域上中游地区气候变化趋势分析

气候变化预估常用的全球气候模式（GCM）难以提供区域或更小尺度上可靠的逐日气候要素序列,针对这一问题,应用统计降尺度模型（statistical downscaling model,SDSM）将HadCM3的模拟数据（包括A2、B2两种情景）处理为具有较高可信度的逐日站点序列。以1961-1990年为基准期,分析了21世纪黄河流域上中游地区未来最高气温、最低气温与年降水量的变化。在A2、B2两种气候变化情景下,日最高气温、日最低气温均呈升高趋势;但A2的变化较显著,日最高气温的升高趋势在景泰站最明显,日最低气温的升高趋势在河曲站最显著。流域平均的年降水量变化范围为-18.2%～13.3%。A2情景下降水量增加和减少的面积基本相等,宝鸡站降水量增加最多;B2情景下大部分区域降水减少,西峰镇降水量减少最显著。     

21世纪黄河流域上中游地区气候变化趋势分析

 气候变化预估常用的全球气候模式（GCM）难以提供区域或更小尺度上可靠的逐日气候要素序列,针对这一问题,应用统计降尺度模型（statistical downscaling model,SDSM）将HadCM3的模拟数据（包括A2、B2两种情景）处理为具有较高可信度的逐日站点序列。以1961-1990年为基准期,分析了21世纪黄河流域上中游地区未来最高气温、最低气温与年降水量的变化。在A2、B2两种气候变化情景下,日最高气温、日最低气温均呈升高趋势;但A2的变化较显著,日最高气温的升高趋势在景泰站最明显,日最低气温的升高趋势在河曲站最显著。流域平均的年降水量变化范围为-18.2%～13.3%。A2情景下降水量增加和减少的面积基本相等,宝鸡站降水量增加最多;B2情景下大部分区域降水减少,西峰镇降水量减少最显著。     

Application of statistical downscaling in GCMs at constructing the map of precipitation in the Mekong River basin

This study used the Statistical Downscaling Model (SDSM) to increase the resolution of the Global Circulation Model (GCM) at forecasting the amount of precipitation in the Mekong River basin. The model was initially calibrated using the reanalysis data by National Centers for Environmental Prediction (NCEP) and the data on observed precipitation. The results of comparison between the SDSM calculations and the observational data were used to generate the distribution of precipitation until 2099 using HadCM3, SRES A2 and B2 scenarios. After total annual precipitation had been downscaled, the percentage change in precipitation was interpolated among the selected stations in order to create precipitation maps. Both A2 and B2 scenario indicate the possibility of remarkable increase in annual precipitation in the Mekong basin, which may amount to 150 and 110%, respectively. The December–January–February precipitation is likely to increase significantly in the most part of the region, and in some areas, almost by three times. On the contrary, the June–July–August precipitation will remarkably decrease in the different parts of the territory under study. As the water resource sector is the backbone of the economics of this region including hydropower and agricultural sector, the changes in the amount of precipitation and its interannual variability can put the usual water business into stress. Thus, proper adaptive measures should be applied both at local and at regional levels for the benefit of all associated countries utilizing the resource of the Mekong River.     

Evaluation of SDSM developed by annual and monthly sub-models for downscaling temperature and precipitation in the Jhelum basin, Pakistan and India

The study evaluates statistical downscaling model (SDSM) developed by annual and monthly sub-models for downscaling maximum temperature, minimum temperature, and precipitation, and assesses future changes in climate in the Jhelum River basin, Pakistan and India. Additionally, bias correction is applied on downscaled climate variables. The mean explained variances of 66, 76, and 11 % for max temperature, min temperature, and precipitation, respectively, are obtained during calibration of SDSM with NCEP predictors, which are selected through a quantitative procedure. During validation, average R ² values by the annual sub-model (SDSM-A)—followed by bias correction using NCEP, H3A2, and H3B2—lie between 98.4 and 99.1 % for both max and min temperature, and 77 to 85 % for precipitation. As for the monthly sub-model (SDSM-M), followed by bias correction, average R ² values lie between 98.5 and 99.5 % for both max and min temperature and 75 to 83 % for precipitation. These results indicate a good applicability of SDSM-A and SDSM-M for downscaling max temperature, min temperature, and precipitation under H3A2 and H3B2 scenarios for future periods of the 2020s, 2050s, and 2080s in this basin. Both sub-models show a mean annual increase in max temperature, min temperature, and precipitation. Under H3A2, and according to both sub-models, changes in max temperature, min temperature, and precipitation are projected as 0.91–3.15 °C, 0.93–2.63 °C, and 6–12 %, and under H3B2, the values of change are 0.69–1.92 °C, 0.56–1.63 °C, and 8–14 % in 2020s, 2050s, and 2080s. These results show that the climate of the basin will be warmer and wetter relative to the baseline period. SDSM-A, most of the time, projects higher changes in climate than SDSM-M. It can also be concluded that although SDSM-A performed well in predicting mean annual values, it cannot be used with regard to monthly and seasonal variations, especially in the case of precipitation unless correction is applied.