Literature

To read

• Brown et al. (2008): review downscaling for Africa (dynamical & statistical, bias correction, advantages, best practices)
• Quintana Seguí et al. (2010): comparing statistical downscaling and bias correction: statistical, quantile mapping, anomaly for hydrology
• Tabor and Williams (2010): downscaling for conservation: CMIP3 with change-factor on CRU
• Zhang (2005): statistically downscaling GCM to station for crop, quantile mapping
• Solomon H. Gebrechorkos, Hülsmann, and Bernhofer (2019): statistical downscaling of GCM for East Africa

Similar approach

• Chakraborty et al. (2020)
  • Euro-CORDEX
  • ECLIPS (European CLimate Index ProjectionS)
  • 80 annual, seasonal and monthly climate variables for two past (1961–1990 and 1991–2010) and five future (2011–2020, 2021–2140, 2041–2060, 2061–2080 and 2081–2100) periods
  • five regional climate models (RCMs)
  • RCP 4.5 and RCP 8.5
  • ECLIPS 1.1 at 0.11° (RCM native)
  • ECLIPS 2.0 30 arcsec downscaled with delta approach
  • tested against independent station data from the European Climate Assessment (ECA) dataset
  • ECLIPS 2.0 (CC 0.78-0.93) > ECLIPS 1.1 (CC 0.36-0.78)
  • Cited: 13
• Shen et al. (2020) - EA-CORDEX - Bias Correction and Ensemble Projections of Temperature Changes over Ten Subregions in CORDEX East Asia - three common bias correction methods: variance scaling, additive scaling, and quantile mapping based on empirical distribution - two multi-model averaging methods:
  • Bayesian model averaging (BMA)
  • simple multi-model averaging (SMA) - calibrating historical (1980–2005) - Future (2006–49) temperature trends - RCP 4.5 and 8.5 - CRU validation
• McGinnis and Mearns (2021) - NA-CORDEX - Five of the major variables used by impacts researchers as well as decision makers have also been bias-corrected - Directly hosted with CORDEX outputs (included on ESGF) - Each RCM community as different experiments corresponding to their questions (ex USA, EU, CA) - Cannon’s MBCn algorithm to bias-correct the data, a multivariate quantile-mapping approach that has good all-around performance for multiple uses and corrects the relationships between variables as well as their individual distributions
• Falco et al. (2018) - SAM-CORDEX - 1990 to 2004 - 0.44° - monthly precipitation and 2-m-temperature - RCM - GCM - ERAi - CRU, UDEL, CPC-UNI evaluation
• A tremendous amount of country / region specific applications

Examples

• S. Gebrechorkos et al. (2023):
  • What: statistically downscaled CMIP6
  • Resolutions: 0.25° (28km) and daily
  • How: BCCAQ
  • Baseline: MSWX & MSWEP
  • Projections: 18 CMIP6 GCMs
  • Scenarios: SSP2-4.5, SSP5-3.4-OS & SSP5-8.5
  • Variables: daily precipitation, air-temperature, maximum and minimum temperature, wind speed, air pressure, and relative humidity
  • Tool: ClimDown R package
  • Evaluation: Pearson, RMSE, bias, SE, Taylor diagram
  • Access: CEDA
  • Note: remove bias & reproduce extreme events
• Navarro-Racines et al. (2020):
  • What: statistically downscaled CMIP6
  • Resolutions: 30” (1km) and 30-year means
  • How: delta method
  • Baseline: WorldClim
  • Projections: 35 CMIP5 GCM
  • Scenarios: RCP 2.6, 4.5, 6.0 & 8.5
  • Variables: mean monthly maximum and minimum temperatures and monthly rainfall
  • Tool: ArcGIS & R
  • Evaluation: perfect sibling
  • Access: WDCC, CCAFS-Climate
  • Note: thin-plate splines interpolation, Probability Density Function (PDF) comparisons
• Platts, Omeny, and Marchant (2014):
  • What: statistically downscaled CORDEX CMIP5
  • Resolutions: 30” (1km) and 20-year means
  • How: change-factor (= delta method)
  • Baseline: CRU, WorldClim, TAMSAT, CHIRPS
  • Projections: 8 GCM CMIP5 x 2 RCM CORDEX
  • Scenarios: RCP4.5 & RCP8.5
  • Variables: monthly 2-m air temperature (mean, minimum and maximum) and monthly rainfall
  • Tool: R & GRASS GIS
  • Evaluation: No
  • Access: York University
  • Note: spline-interpolated

Downscaling

Bias correction

• delta or change-factor (Platts, Omeny, and Marchant 2014; Navarro-Racines et al. 2020)

Statistical

• SDSM: Statistical DownScaling Model (R. L. Wilby, Dawson, and Barrow 2002; R. L. Wilby and Dawson 2012)

Quantile

• BCCAQ: Bias Correction Constructed Analogues with Quantile mapping reordering (Cannon, Sobie, and Murdock 2015; Werner and Cannon 2016)
• CA: Constructed Analogues (Maurer et al. 2010)
• CI: Climate Imprint (Hunter and Meentemeyer 2005)
• DQM: Quantile Delta Mapping (Cannon, Sobie, and Murdock 2015)
• CDFt: cumulative distribution function transform (R CDFt) (Lanzante et al. 2019)

Other

• TLFN: time-lagged feed-forward neural network or temporal neural network (Coulibaly, Dibike, and Anctil 2005)
• kriging (KrigR) (Davy and Kusch 2021)
• Dynamical downscaling (RCM)

Climate data

https://climatedataguide.ucar.edu/type/satellite-data-products

Reanalysis

• ERA5-Land: reanalysis dataset from European Centre for Medium-Range Weather Forecasts (ECMWF) (Muñoz-Sabater et al. 2021)
  • Resolutions: 0.1° (9km) & hourly (daily and monthly averages)
  • Period: 1950-present
  • Area: global
  • Variables: 53 including temperature and precipitation
  • Provider: CDS
  • Projections: No (but CMIP & CORDEX on CDS)
  • Tools: python cdsapi, R ewcmfr
  • Note:
• CHELSA 2.1: Climatologies at High resolution for the Earth’s Land Surface Areas (Karger et al. 2017)
  • Resolutions: 30” (1km) & 20-year means (daily & monthly available)
  • Period: 1981-2100
  • Area: global
  • Variables: pr, rsds, tas, tasmax, tasmin, bioclim
  • Provider: CHELSA
  • Projections: downscaled CMIP5 & CMIP6
  • Tools: R ntbox, chelsaDL
  • Note:
• WorldClim 2: 1-km spatial resolution climate surfaces for global land areas (Fick and Hijmans 2017)
  • Resolutions: 30” (1km) & 30-year means
  • Period: 1970-2000
  • Area: global
  • Variables: tasmin, tasmax, tas, pr, rsds, ws, vapr, bioclim
  • Provider: WorldClim
  • Projections: CMIP6 23 GCM 4 SSP 20-year means
  • Tools: R raster & geodata
  • Note:
• CRU TS 4: monthly high-resolution gridded multivariate climate dataset (Climatic Research Unit Time Series) (Harris et al. 2020)
  • Resolutions: 0.5° (45km) & monthly
  • Period: 1901–2018
  • Area: global
  • Variables: cld, dtr, frs, pet, pre, tmn, tmp, tmx, vap, wet
  • Provider: CRU
  • Projections: No
  • Tools: R cruts
  • Note: angular-distance weighting (ADW)
• CHIRPS: Rainfall Estimates from Rain Gauge and Satellite Observations (Funk et al. 2015)
  • Resolutions: 0.05°(900m) & daily, pentadal, and monthly
  • Period: 1981-present
  • Area: global
  • Variables: rainfall
  • Provider: CHIRPS
  • Projections: No
  • Tools: R chirps
  • Note: infrared Cold Cloud Duration
• JRA-55: the Japanese 55-year Reanalysis (Kobayashi and Iwasaki 2016)
  • Resolutions:
  • Period: 1958-present
  • Area: global
  • Variables:
  • Provider: JRA
  • Projections: No
  • Tools:
  • Note:
• TerraClimate: a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015 (Abatzoglou et al. 2018)
  • Resolutions: 0.04° (4km) & monthly
  • Period: 1958-2020
  • Area: global
  • Variables: Maximum temperature, minimum temperature, vapor pressure, precipitation accumulation, downward surface shortwave radiation, wind-speed
  • Provider: TerraClimate
  • Projections: No
  • Tools: R TerraClimate
  • Note: derived variables accessible, derived from WorldClim + CRU TS + JRA55
• GloH2O MSWX: Multi-Source Weather (Beck et al. 2022) & MSWEP: Multi-Source Weighted-Ensemble Precipitation (Beck et al. 2017; Beck et al. 2019)
  • Resolutions: 0.1° (9km) & 3-hourly
  • Period: 1979-present
  • Area: global
  • Variables: precipitation
  • Provider: GloH2O
  • Projections: No
  • Tools:
  • Note: gauge, ERA5, HydroGFD, PGF, WFDE5, …

Observations

• GHCNd: Historical Climatology Network Daily (Menne et al. 2012)
• GSOD: Global Summary of the Day (H Sparks, Hengl, and Nelson 2017)
• GPCC: Global Precipitation Climatology Centre (Becker et al. 2013)

Satellite

• TAMSAT: Tropical Applications of Meteorology using Satellite data and ground-based observations (Maidment et al. 2017)
• CMORPH: Climate Prediction Center morphing technique (Joyce et al. 2004)
• TRMM: Tropical Rainfall Measuring Mission (Kummerow et al. 1998)
• TMPA: Multi-satellite Precipitation Analysis (Huffman et al. 2009)
• GSMaP: Global Satellite Mapping (Okamoto et al., n.d.)

Climate projections

• CMIP5: the fifth phase of Coupled Model Intercomparison Project (Taylor, Stouffer, and Meehl 2012)
• CORDEX (CMIP5 based): Coordinated Regional Downscaling Experiment (Giorgi and Gutowski 2015)
• CMIP6: the sixth phase of Coupled Model Intercomparison Project (Tokarska et al. 2020)
• CMIP6 HighResMIP: High Resolution Model Intercomparison Project (Haarsma et al. 2016; Liang-Liang, Jian, and Ru-Cong 2022)

Providers

• CDS: Climate Data Store including CMIP (not HighRes), ERA5-Land, CORDEX (not all), …
• ESGF: Earth System Grid Federation for different providers (e.g. UK-CEDA, IPSL, LLNL, …) including HighResMIP, CMIP, and CORDEX
• WDCC: World Data Center for Climate
• CCAFS-Climate: global and regional future high-resolution climate datasets

Scenarios

• RCP: Representative Concentration Pathways
  • Critics of RCP 8.5 used wrongly as buisness as usual in Hausfather and Peters (2020)
• SSP: Shared Socio-economic Pathways

Selection

Compromise for computation limitations.

• (1) initial selection of climate models based on the range of projected changes in climatic means, (2) refined selection based on the range of projected changes in climatic extremes and (3) final selection based on the climate model skill to simulate past climate (Lutz et al. 2016)
• envelope approach: representing the diversity of projections
• past-performance approach: capacity to reproduce past/present climate

Evaluation

• TSS: Taylor Skill Score (Liang-Liang, Jian, and Ru-Cong 2022)
• IA: index of agreement (Solomon Hailu Gebrechorkos, Hülsmann, and Bernhofer 2018)
• CC: Pearson correlation coefficient
• RMSE: Root mean square error
• MAE: Mean absolute error
• R: Relative bias
• SD: Standard deviation
• Taylor diagram (Taylor 2001)
• PS: perfect sibling (Hawkins et al. (2013)]
• Extreme indices from Expert Team on Climate Change Detection and Indices (ETCCDI) (Karl, Nicholls, and Ghazi 1999)

Tools

Standalone

• SAGA GIS
• CDO: Climate Data Operators
• singularity

Python

• xarray
• xesmf
• salem
• rioxarray
• snakemake

R

• terra
• netcdf4
• ClimDown: downscaling with Bias Correction/Constructed Analogues with Quantile mapping reordering (BCCAQ) (Hiebert et al. 2018)
• climdex.pcic: climate extremes indices
• KrigR (Davy and Kusch 2021)
• CDFt
• GSODR (H Sparks, Hengl, and Nelson 2017)

Abatzoglou, John T., Solomon Z. Dobrowski, Sean A. Parks, and Katherine C. Hegewisch. 2018. “TerraClimate, a High-Resolution Global Dataset of Monthly Climate and Climatic Water Balance from 19582015.” Scientific Data 5 (1). https://doi.org/10.1038/sdata.2017.191.

Beck, Hylke E., Albert I. J. M. van Dijk, Pablo R. Larraondo, Tim R. McVicar, Ming Pan, Emanuel Dutra, and Diego G. Miralles. 2022. “MSWX: Global 3-Hourly 0.1° Bias-Corrected Meteorological Data Including Near-Real-Time Updates and Forecast Ensembles.” Bulletin of the American Meteorological Society 103 (3): E710–32. https://doi.org/10.1175/bams-d-21-0145.1.

Beck, Hylke E., Albert I. J. M. van Dijk, Vincenzo Levizzani, Jaap Schellekens, Diego G. Miralles, Brecht Martens, and Ad de Roo. 2017. “MSWEP: 3-Hourly 0.25° Global Gridded Precipitation (19792015) by Merging Gauge, Satellite, and Reanalysis Data.” Hydrology and Earth System Sciences 21 (1): 589–615. https://doi.org/10.5194/hess-21-589-2017.

Beck, Hylke E., Eric F. Wood, Ming Pan, Colby K. Fisher, Diego G. Miralles, Albert I. J. M. van Dijk, Tim R. McVicar, and Robert F. Adler. 2019. “MSWEP V2 Global 3-Hourly 0.1° Precipitation: Methodology and Quantitative Assessment.” Bulletin of the American Meteorological Society 100 (3): 473–500. https://doi.org/10.1175/bams-d-17-0138.1.

Becker, A., P. Finger, A. Meyer-Christoffer, B. Rudolf, K. Schamm, U. Schneider, and M. Ziese. 2013. “A Description of the Global Land-Surface Precipitation Data Products of the Global Precipitation Climatology Centre with Sample Applications Including Centennial (Trend) Analysis from 1901present.” Earth System Science Data 5 (1): 71–99. https://doi.org/10.5194/essd-5-71-2013.

Brown, Casey, Arthur M. Greene, Paul J. Block, and Alessandra Giannini. 2008. “Review of Downscaling Methodologies for Africa Climate Applications.” Columbia University. https://doi.org/10.7916/D8M04C88.

Cannon, Alex J., Stephen R. Sobie, and Trevor Q. Murdock. 2015. “Bias Correction of GCM Precipitation by Quantile Mapping: How Well Do Methods Preserve Changes in Quantiles and Extremes?” Journal of Climate 28 (17): 6938–59. https://doi.org/10.1175/jcli-d-14-00754.1.

Chakraborty, Debojyoti, Laura Dobor, Anita Zolles, Tomáš Hlásny, and Silvio Schueler. 2020. “High-Resolution Gridded Climate Data for Europe Based on Bias-Corrected EURO-CORDEX: The ECLIPS Dataset.” Geoscience Data Journal 8 (2): 121–31. https://doi.org/10.1002/gdj3.110.

Coulibaly, Paulin, Yonas B. Dibike, and François Anctil. 2005. “Downscaling Precipitation and Temperature with Temporal Neural Networks.” Journal of Hydrometeorology 6 (4): 483–96. https://doi.org/10.1175/jhm409.1.

Davy, Richard, and Erik Kusch. 2021. “Reconciling High Resolution Climate Datasets Using KrigR.” Environmental Research Letters 16 (12): 124040. https://doi.org/10.1088/1748-9326/ac39bf.

Falco, Magdalena, Andrea F. Carril, Claudio G. Menéndez, Pablo G. Zaninelli, and Laurent Z. X. Li. 2018. “Assessment of CORDEX Simulations over South America: Added Value on Seasonal Climatology and Resolution Considerations.” Climate Dynamics 52 (7-8): 4771–86. https://doi.org/10.1007/s00382-018-4412-z.

Fick, Stephen E., and Robert J. Hijmans. 2017. “WorldClim 2: New 1-Km Spatial Resolution Climate Surfaces for Global Land Areas.” International Journal of Climatology 37 (12): 4302–15. https://doi.org/10.1002/joc.5086.

Funk, Chris, Pete Peterson, Martin Landsfeld, Diego Pedreros, James Verdin, Shraddhanand Shukla, Gregory Husak, et al. 2015. “The Climate Hazards Infrared Precipitation with Stationsa New Environmental Record for Monitoring Extremes.” Scientific Data 2 (1). https://doi.org/10.1038/sdata.2015.66.

Gebrechorkos, Solomon Hailu, Stephan Hülsmann, and Christian Bernhofer. 2018. “Evaluation of Multiple Climate Data Sources for Managing Environmental Resources in East Africa.” Hydrology and Earth System Sciences 22 (8): 4547–64. https://doi.org/10.5194/hess-22-4547-2018.

Gebrechorkos, Solomon H., Stephan Hülsmann, and Christian Bernhofer. 2019. “Statistically Downscaled Climate Dataset for East Africa.” Scientific Data 6 (1). https://doi.org/10.1038/s41597-019-0038-1.

Gebrechorkos, Solomon, Julian Leyland, Louise Slater, Michel Wortmann, Philip J. Ashworth, Georgina L. Bennett, Richard Boothroyd, et al. 2023. “A High-Resolution Daily Global Dataset of Statistically Downscaled CMIP6 Models for Climate Impact Analyses.” Scientific Data 10 (1). https://doi.org/10.1038/s41597-023-02528-x.

Giorgi, Filippo, and William J. Gutowski. 2015. “Regional Dynamical Downscaling and the CORDEX Initiative.” Annual Review of Environment and Resources 40 (1): 467–90. https://doi.org/10.1146/annurev-environ-102014-021217.

H Sparks, Adam, Tomislav Hengl, and Andrew Nelson. 2017. “GSODR: Global Summary Daily Weather Data in r.” The Journal of Open Source Software 2 (10): 177. https://doi.org/10.21105/joss.00177.

Haarsma, Reindert J., Malcolm J. Roberts, Pier Luigi Vidale, Catherine A. Senior, Alessio Bellucci, Qing Bao, Ping Chang, et al. 2016. “High Resolution Model Intercomparison Project (HighResMIP V1.0) for CMIP6.” Geoscientific Model Development 9 (11): 4185–4208. https://doi.org/10.5194/gmd-9-4185-2016.

Harris, Ian, Timothy J. Osborn, Phil Jones, and David Lister. 2020. “Version 4 of the CRU TS Monthly High-Resolution Gridded Multivariate Climate Dataset.” Scientific Data 7 (1). https://doi.org/10.1038/s41597-020-0453-3.

Hausfather, Zeke, and Glen P. Peters. 2020. “Emissions the ‘Business as Usual’ Story Is Misleading.” Nature 577 (7792): 618–20. https://doi.org/10.1038/d41586-020-00177-3.

Hawkins, Ed, Thomas M. Osborne, Chun Kit Ho, and Andrew J. Challinor. 2013. “Calibration and Bias Correction of Climate Projections for Crop Modelling: An Idealised Case Study over Europe.” Agricultural and Forest Meteorology 170 (March): 19–31. https://doi.org/10.1016/j.agrformet.2012.04.007.

Hiebert, James, Alex J. Cannon, Trevor Murdock, Stephen Sobie, and Arelia Werner. 2018. “ClimDown: Climate Downscaling in r.” The Journal of Open Source Software 3 (22): 360. https://doi.org/10.21105/joss.00360.

Huffman, George J., Robert F. Adler, David T. Bolvin, and Eric J. Nelkin. 2009. “The TRMM Multi-Satellite Precipitation Analysis (TMPA).” In, 3–22. Springer Netherlands. https://doi.org/10.1007/978-90-481-2915-7_1.

Hunter, Richard D., and Ross K. Meentemeyer. 2005. “Climatologically Aided Mapping of Daily Precipitation and Temperature.” Journal of Applied Meteorology 44 (10): 1501–10. https://doi.org/10.1175/jam2295.1.

Joyce, Robert J., John E. Janowiak, Phillip A. Arkin, and Pingping Xie. 2004. “CMORPH: A Method That Produces Global Precipitation Estimates from Passive Microwave and Infrared Data at High Spatial and Temporal Resolution.” Journal of Hydrometeorology 5 (3): 487–503. https://doi.org/10.1175/1525-7541(2004)005<0487:camtpg>2.0.co;2.

Karger, Dirk Nikolaus, Olaf Conrad, Jürgen Böhner, Tobias Kawohl, Holger Kreft, Rodrigo Wilber Soria-Auza, Niklaus E. Zimmermann, H. Peter Linder, and Michael Kessler. 2017. “Climatologies at High Resolution for the Earth’s Land Surface Areas.” Scientific Data 4 (1). https://doi.org/10.1038/sdata.2017.122.

Karl, Thomas R., Neville Nicholls, and Anver Ghazi. 1999. “CLIVAR/GCOS/WMO Workshop on Indices and Indicators for Climate Extremes Workshop Summary.” In, 3–7. Springer Netherlands. https://doi.org/10.1007/978-94-015-9265-9_2.

Kobayashi, Chiaki, and Toshiki Iwasaki. 2016. “Brewer-Dobson Circulation Diagnosed from JRA-55.” Journal of Geophysical Research: Atmospheres 121 (4): 1493–1510. https://doi.org/10.1002/2015jd023476.

Kummerow, Christian, William Barnes, Toshiaki Kozu, James Shiue, and Joanne Simpson. 1998. “The Tropical Rainfall Measuring Mission (TRMM) Sensor Package.” Journal of Atmospheric and Oceanic Technology 15 (3): 809–17. https://doi.org/10.1175/1520-0426(1998)015<0809:ttrmmt>2.0.co;2.

Lanzante, John R., Mary Jo Nath, Carolyn E. Whitlock, Keith W. Dixon, and Dennis Adams-Smith. 2019. “Evaluation and Improvement of Tail Behaviour in the Cumulative Distribution Function Transform Downscaling Method.” International Journal of Climatology 39 (4): 2449–60. https://doi.org/10.1002/joc.5964.

Liang-Liang, Li, Li Jian, and Yu Ru-Cong. 2022. “Evaluation of CMIP6 HighResMIP Models in Simulating Precipitation over Central Asia.” Advances in Climate Change Research 13 (1): 1–13. https://doi.org/10.1016/j.accre.2021.09.009.

Lutz, Arthur F., Herbert W. ter Maat, Hester Biemans, Arun B. Shrestha, Philippus Wester, and Walter W. Immerzeel. 2016. “Selecting Representative Climate Models for Climate Change Impact Studies: An Advanced Envelope-Based Selection Approach.” International Journal of Climatology 36 (12): 3988–4005. https://doi.org/10.1002/joc.4608.

Maidment, Ross I, David Grimes, Emily Black, Elena Tarnavsky, Matthew Young, Helen Greatrex, Richard P Allan, et al. 2017. “A New, Long-Term Daily Satellite-Based Rainfall Dataset for Operational Monitoring in Africa.” Scientific Data 4 (1). https://doi.org/10.1038/sdata.2017.63.

Maurer, E. P., H. G. Hidalgo, T. Das, M. D. Dettinger, and D. R. Cayan. 2010. “The Utility of Daily Large-Scale Climate Data in the Assessment of Climate Change Impacts on Daily Streamflow in California.” Hydrology and Earth System Sciences 14 (6): 1125–38. https://doi.org/10.5194/hess-14-1125-2010.

McGinnis, Seth, and Linda Mearns. 2021. “Building a Climate Service for North America Based on the NA-CORDEX Data Archive.” Climate Services 22 (April): 100233. https://doi.org/10.1016/j.cliser.2021.100233.

Menne, Matthew J., Imke Durre, Russell S. Vose, Byron E. Gleason, and Tamara G. Houston. 2012. “An Overview of the Global Historical Climatology Network-Daily Database.” Journal of Atmospheric and Oceanic Technology 29 (7): 897–910. https://doi.org/10.1175/jtech-d-11-00103.1.

Muñoz-Sabater, Joaquín, Emanuel Dutra, Anna Agustí-Panareda, Clément Albergel, Gabriele Arduini, Gianpaolo Balsamo, Souhail Boussetta, et al. 2021. “ERA5-Land: A State-of-the-Art Global Reanalysis Dataset for Land Applications.” Earth System Science Data 13 (9): 4349–83. https://doi.org/10.5194/essd-13-4349-2021.

Navarro-Racines, Carlos, Jaime Tarapues, Philip Thornton, Andy Jarvis, and Julian Ramirez-Villegas. 2020. “High-Resolution and Bias-Corrected CMIP5 Projections for Climate Change Impact Assessments.” Scientific Data 7 (1). https://doi.org/10.1038/s41597-019-0343-8.

Okamoto, K.i., T. Ushio, T. Iguchi, N. Takahashi, and K. Iwanami. n.d. “The Global Satellite Mapping of Precipitation (GSMaP) Project.” Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS ’05. https://doi.org/10.1109/igarss.2005.1526575.

Platts, Philip J., Peter A. Omeny, and Rob Marchant. 2014. “AFRICLIM: High-Resolution Climate Projections for Ecological Applications in Africa.” African Journal of Ecology 53 (1): 103–8. https://doi.org/10.1111/aje.12180.

Quintana Seguí, P., A. Ribes, E. Martin, F. Habets, and J. Boé. 2010. “Comparison of Three Downscaling Methods in Simulating the Impact of Climate Change on the Hydrology of Mediterranean Basins.” Journal of Hydrology 383 (1-2): 111–24. https://doi.org/10.1016/j.jhydrol.2009.09.050.

Shen, Chenwei, Qingyun Duan, Chiyuan Miao, Chang Xing, Xuewei Fan, Yi Wu, and Jingya Han. 2020. “Bias Correction and Ensemble Projections of Temperature Changes over Ten Subregions in CORDEX East Asia.” Advances in Atmospheric Sciences 37 (11): 1191–1210. https://doi.org/10.1007/s00376-020-0026-6.

Tabor, Karyn, and John W. Williams. 2010. “Globally Downscaled Climate Projections for Assessing the Conservation Impacts of Climate Change.” Ecological Applications 20 (2): 554–65. https://doi.org/10.1890/09-0173.1.

Taylor, Karl E. 2001. “Summarizing Multiple Aspects of Model Performance in a Single Diagram.” Journal of Geophysical Research: Atmospheres 106 (D7): 7183–92. https://doi.org/10.1029/2000jd900719.

Taylor, Karl E., Ronald J. Stouffer, and Gerald A. Meehl. 2012. “An Overview of CMIP5 and the Experiment Design.” Bulletin of the American Meteorological Society 93 (4): 485–98. https://doi.org/10.1175/bams-d-11-00094.1.

Tokarska, Katarzyna B., Martin B. Stolpe, Sebastian Sippel, Erich M. Fischer, Christopher J. Smith, Flavio Lehner, and Reto Knutti. 2020. “Past Warming Trend Constrains Future Warming in CMIP6 Models.” Science Advances 6 (12). https://doi.org/10.1126/sciadv.aaz9549.

Werner, Arelia T., and Alex J. Cannon. 2016. “Hydrologic Extremes an Intercomparison of Multiple Gridded Statistical Downscaling Methods.” Hydrology and Earth System Sciences 20 (4): 1483–1508. https://doi.org/10.5194/hess-20-1483-2016.

Wilby, R. L., and C. W. Dawson. 2012. “The Statistical DownScaling Model: Insights from One Decade of Application.” International Journal of Climatology 33 (7): 1707–19. https://doi.org/10.1002/joc.3544.

Wilby, R. L, C. W Dawson, and E. M Barrow. 2002. “Sdsm a Decision Support Tool for the Assessment of Regional Climate Change Impacts.” Environmental Modelling & Software 17 (2): 145–57. https://doi.org/10.1016/s1364-8152(01)00060-3.

Zhang, X.-C. 2005. “Spatial Downscaling of Global Climate Model Output for Site-Specific Assessment of Crop Production and Soil Erosion.” Agricultural and Forest Meteorology 135 (1-4): 215–29. https://doi.org/10.1016/j.agrformet.2005.11.016.