Content: Machine generated contents note: ch. 1 Introduction --
1.1. Why Statistical Downscaling? --
1.2. Climate Models --
1.3. Statistical Downscaling --
1.4. Selection of Model Scheme --
1.5. Structure of Chapters --
1.6. Summary and Conclusion --
ch. 2 Statistical Background --
2.1. Probability and Statistics --
2.1.1. Probabilistic Theory --
2.1.1.1. Probability Density Function and Cumulative Distribution Function --
2.1.1.2. Descriptors of Random Variables --
2.1.2. Discrete Probability Distributions --
2.1.2.1. Bernoulli Distribution --
2.1.2.2. Binomial Distribution --
2.1.3. Continuous Probability Distributions --
2.1.3.1. Normal Distribution and Lognormal Distributions --
2.1.3.2. Exponential and Gamma Distributions --
2.1.3.3. Generalized Extreme Value and Gumbel Distribution --
2.1.4. Parameter Estimation for Probability Distributions --
2.1.4.1. Method of Moments --
2.1.4.2. Maximum Likelihood Estimation --
2.1.5. Histogram and Empirical Distribution --
2.2. Multivariate Random Variables --
2.2.1. Multivariate Normal Distribution and Its Conditional Distribution --
2.2.2. Covariance and Correlation --
2.3. Random Simulation --
2.3.1. Monte Carlo Simulation and Uniform Random Number --
2.3.2. Simulation of Probability Distributions --
2.4. Metaheuristic Algorithm --
2.4.1. Harmony Search --
2.5. Summary and Conclusion --
ch. 3 Data and Format Description --
3.1. GCMData --
3.2. Reanalysis Data --
3.3. RCMData --
3.4. Summary and Conclusion --
ch. 4 Bias Correction --
4.1. Why Bias Correction? --
4.2. Occurrence Adjustment for Precipitation Data --
4.3. Empirical Adjustment (Delta Method) --
4.4. Quantile Mapping --
4.4.1. General Quantile Mapping --
4.4.2. Nonparametric Quantile Mapping --
4.4.3. Quantile Delta Mapping --
4.5. Summary and Comparison --
ch. 5 Regression Downscalings --
5.1. Linear Regression Based Downscaling --
5.1.1. Simple Linear Regression --
5.1.1.1. Significance Test --
5.1.2. Multiple Linear Regression --
5.2. Predictor Selection --
5.2.1. Stepwise Regression --
5.2.2. Least Absolute Shrinkage and Selection Operator --
5.3. Nonlinear Regression Modeling --
5.3.1. Artificial Neural Network --
5.4. Summary and Conclusion --
ch. 6 Weather Generator Downscaling --
6.1. Mathematical Background --
6.1.1. Autoregressive Models --
6.1.2. Multivariate Autoregressive Model --
6.1.3. Markov Chain --
6.2. Weather Generator --
6.2.1. Model Fitting --
6.2.1.1. Precipitation --
6.2.1.2. Weather Variables (Tmax, Tmin, SR) --
6.2.2. Simulation of Weather Variables --
6.2.2.1. Precipitation --
6.2.2.2. Weather Variables (Tmax, Tmin, SR) --
6.2.3. Implementation of Downscaling --
6.3. Nonparametric Weather Generator --
6.3.1. Simulation under Current Climate --
6.3.2. Simulation under Future Climate Scenarios --
6.4. Summary and Conclusion --
ch. 7 Weather-Type Downscaling --
7.1. Classification of Weather Types --
7.1.1. Empirical Weather Type --
7.1.2. Objective Weather Type --
7.2. Generation of Daily Rainfall Sequences --
7.3. Future Climate with Weather-Type Downscaling --
7.4. Summary and Conclusion --
ch. 8 Temporal Downscaling --
8.1. Background --
8.1.1. K-Nearest Neighbor Resampling --
8.2. Daily to Hourly Downscaling --
8.3. Summary and Conclusion --
ch. 9 Spatial Downscaling --
9.1. Mathematical Background --
9.1.1. Bilinear Interpolation --
9.1.2. Nearest Neighbor Interpolation --
9.2. Bias Correction and Spatial Downscaling --
9.3. Bias Correction and Constructed Analogues --
9.4. Bias Correction and Stochastic Analogue --
9.5. Summary and Comparison.