Background ========== These articles explain the concepts, theory, and design decisions behind scikit-downscale and statistical downscaling methods. .. note:: Background articles are explanatory and conceptual. They provide context and deeper understanding but are not task-oriented. Coming Soon ----------- We are actively developing background articles. Future topics will include: Statistical Downscaling Concepts ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ * What is statistical downscaling? * Bias correction vs. spatial disaggregation * When to use statistical vs. dynamical downscaling * Understanding quantile mapping * Stationarity assumptions in downscaling Method Comparisons ~~~~~~~~~~~~~~~~~~ * Comparing downscaling methods: strengths and weaknesses * Performance metrics for evaluating downscaling * Handling extremes in downscaling * Temporal and spatial consistency Design and Architecture ~~~~~~~~~~~~~~~~~~~~~~~ * Why scikit-learn's API? * Pointwise vs. spatial downscaling approaches * Integration with Xarray and Dask * Extensibility and custom methods Related Documentation --------------------- While we develop these articles, you might find these resources helpful: * :doc:`../tutorials/index` - Hands-on learning with examples * :doc:`../roadmap` - Project development roadmap * `Scientific References `_ - Key papers and methods External Resources ------------------ For broader context on statistical downscaling: * `Gutmann et al. (2014) `_ - Intercomparison of statistical downscaling methods * `Maraun et al. (2010) `_ - Precipitation downscaling under climate change * `IPCC AR6 WG1 Chapter 10 `_ - Linking global to regional climate change