pyClim-SDM - Service for generation of statistically downscaled climate change projections supporting national adaptation strategies

Sep 2, 2026

Upload by Mason

The climate change impact and adaptation communities require future scenarios with sufficiently high resolution, often produced by applying Statistical Downscaling Models (SDMs) over global climate models. A range of SDMs exists, and suitability can vary by application, so evaluation and generation tools are essential. This paper introduces the ‘pyClim-SDM’ service, offering an intuitive graphical interface to generate and assess downscaled daily data for surface variables and custom target variables, including multivariable indexes while avoiding intervariable inconsistencies. An example using a Fire Weather Index is provided.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages13

Reading Time33 min

AccessFree to download

Service overview

Graphical interface and workflow

Supported variables and target flexibility

Downscaling methods by families

Example: Fire Weather Index

Role in national adaptation strategy

| pyClim-SDM: Service for generation of statistically downscaled climate change projections supporting national adaptation strategies | | | | | --- | --- | --- | --- | | Alfonso Hernanza, *, Carlos Correa a, Juan Andre´s García-Valerob, Marta Domínguez a, Esteban Rodríguez-Guisado a, Ernesto Rodríguez-Camino a
a Spanish Meteorological Agency (AEMET), Madrid 28040, Spain b AEMET, Murcia 30107, Spain | | | | | A R T I C L E I N F O | | A B S T R A C T | | | Keywords:
Statistical downscaling Climate service
Climate projections Graphical user interface Software | | The climate change impact and adaptation communities need future scenarios with sufficient high resolution, which are frequently achieved by applying Statistical Downscaling Models (SDMs) over global climate models. A large variety of SDMs exists, and some can be more suitable than others for each specific purpose. For this reason, it is important to develop tools to facilitate the evaluation and generation of downscaled scenarios following different approaches. In this paper we present a service, ‘pyClim-SDM’, which allows users to generate and evaluate their own downscaled scenarios with a very simple and user-friendly graphical interface. This tool includes a large collection of state-of-the-art methods belonging to different families to downscale daily data of the following surface variables: temperature, precipitation, wind, relative humidity and cloud coverage. Additionally, the software is prepared to be applied over any other user-defined target variable. Thus, multivariable indexes can be tackled as target variables themselves, instead of being calculated from the downscaled primary variables. With this possibility, potential intervariable inconsistencies are avoided. An application example for a Fire Weather Index, dependent on temperature, wind, humidity and precipitation, is shown. The service herepresented -mainly based on a new downscaling software and a user-friendly graphical interface- is an essential piece for evaluating and generating high-resolution projection data within the Spanish national climate change adaptation strategy which includes, among other elements, a common database for all sectors, viewer and data distribution portal, etc. | | | Model | Institution | References | | --- | --- | --- | | ACCESS- | Commonwealth Scientific and Industrial | Bi et al. (2020) | | CM2 | Research Org. (CSIRO) and Bureau of Meteorology (BoM), Australia | | | CanESM5 | Canadian Centre for Climate Modelling and Analysis, Canada | Swart et al.(2019) | | EC-Earth3 | EC-Earth consortium, Europe | Do¨scher et al.(2021) | | INM-CM5-0 | Institute of Numerical Mathematics, Russia | Volodin et al.(2017) | | MIROC6 | Research Center for Environmental Modeling and Application, Japan | Tatebe et al.(2019) | | MPI-ESM1- | Max-Planck-Institut (MPI) for Meteorology, | Müller et al. | | 2-HR | Germany | (2018) | | MRI-ESM2-0 | Meteorological Research Institute, Tsukuba, Japan | Yukimoto et al.(2019) | | Target variable | Predictors | | --- | --- | | tas, tasmax,
tasmin | tas, ua850, ua500, va850, va500, ta850, ta500 | | pr | psl, ua850, ua500, va850, va500, hur850, hur500, K_index, TT_index | | uas, vas, sfcWind | uas, vas, ua850, ua500, va850, va500, ta850, ta500 | | hurs | tas, hurs, ta850, ta500, hur850, hur500 | | clt | clt | | FWI | psl, uas, vas, tas, hurs, ua850, ua500, va850, va500, ta850, ta500, hurs850, hurs500, K_index, TT_index | | Family | Method | Temperature | | | Precipitation | | | | --- | --- | --- | --- | --- | --- | --- | --- | | | | Training Disk | Training Time | Downscaling Time | Training Disk | Training Time | Downscaling Time | | RAW (interpolation) | RAW
RAW-BIL | –
- | –
- | ip
ip | –
- | –
- | ip
ip | | MOS (Model Output Statistics) | QM | - | - | ip | - | - | ip | | | DQM | - | - | ip | - | - | ip | | | QDM | - | - | ip | - | - | ip | | | PSDM | - | - | ip | - | - | ip | | ANA / WT (Analogs/We

FAQs

What is pyClim-SDM designed to help users do?

pyClim-SDM helps users generate and evaluate statistically downscaled climate change scenarios through a simple, user-friendly graphical interface.

Which surface variables can be downscaled with the service?

The service supports downscaling daily data for temperature, precipitation, wind, relative humidity, and cloud coverage.

Can users choose target variables beyond the primary downscaled outputs?

Yes. Users can apply the software to any other user-defined target variable, including multivariable indexes treated directly as target variables.

Sep 2, 2026

Upload by Mason