CFT — Climate Forecasting Toolbox

A Python-based tool for statistical climate forecasting, developed by the SADC Climate Services Centre (CSC).

Table of Contents

• Introduction
• Modules
  • Download
  • Forecast
  • Zoning, Verification, Synthesis
• Requirements
• Installation
• Usage — GUI
• Usage — without GUI
• Credits
• Development History

Introduction

CFT is a Python-based tool for statistical climate forecasting. From version 5, it consists of five modules:

Modules

Download module

Downloads the following data types:

1. Gridded predictors from CDS — SST, GPH, rainfall, and air temperature, from all models available on CDS as of July 2026 (except UKMO)
2. Gridded predictand from CDS — rainfall and temperature from the CDS ERA5 dataset
3. Gridded predictors from IRIDL (legacy) — SST
4. Gridded predictand from IRIDL (legacy) — CHIRPS precipitation
5. Gridded forecast/hindcast data from IRIDL (legacy) — SST, precipitation, and geopotential height, for three NMME models operational as of August 2025
6. Teleconnection indices from JMA (chosen for lowest latency) — IOD, Niño3, Niño4

Data are downloaded to a user-selected directory and converted to a format ingestible by the rest of CFT.

Forecast module

Predictand types: gridded data, or station data

Predictor types:

• Observed teleconnection indices
• Observed gridded data (SST)
• Forecasted gridded fields (not yet implemented)

Forecast output levels:

• Grid — if the predictand is gridded
• Zones — if the predictand is gridded or station data; data are spatially aggregated into a zonal average and the forecast is generated for that average
• Points — if the predictand is station data

Pre-processing approaches:

Statistical models: OLS regression, MLP regression, Decision Trees, Random Forest, Lasso regression, Ridge regression

Forecast types: deterministic, and tercile probabilistic. The tercile forecast is also presented as a 4-category forecast, splitting the normal category into normal-to-above and normal-to-below (no probabilities are allocated to these two split categories).

Cross-validation: all forecasts are cross-validated, using either leave-one-out or k-fold. Skill indices are calculated from out-of-fold predictions:

• ROC score (above / below / normal)
• RPSS
• Correlation
• Heidke skill score (highest-probability category)
• Ignorance score
• Reliability score
• Brier skill score
• 2AFC

Calibration: probabilistic forecasts are calibrated via quantile mapping — a quantile transform is developed from overlapping hindcast and observed predictand data, then applied to both hindcast and forecast.

Other modules — Zoning, Verification, Synthesis

The zoning module was adapted to run under the v5 environment in v5.1.

Note: the verification and synthesis modules have not yet been updated to the same extent as the modules above, and aren't documented in detail here yet. (more to follow)

Requirements

• Python 3.12 (recommended for v6.0)
• All Python package dependencies are installed automatically via pip — see pyproject.toml for the full list. Notably: numpy, pandas, geopandas, xarray, rioxarray, scipy, shapely, rasterio, rasterstats, scikit-learn, netCDF4, cftime, geocube, GDAL, matplotlib, cartopy, PyQt5, cdsapi.

Installation

1. Set up a Python environment

Installation should be done into a dedicated Python environment. You'll need one of:

• Anaconda (full framework, >1GB)
• Miniforge (installs both Conda and Mamba)
• Micromamba (lightweight; for advanced users)

Once installed, create a dedicated environment:

mamba create --name cft-env python=3.12

2. Install GDAL first, separately

pip does not reliably handle the GDAL system library, so it must be pre-installed via conda/mamba before installing the sadc-cft package itself:

mamba activate cft-env
mamba install -c conda-forge gdal=3.13.2

3. Install CFT with pip

pip install sadc-cft

4. Confirm the installation

python -m cft

This should launch the main CFT GUI.

Usage — GUI

Activate the environment first, as always:

mamba activate cft-env

Launch the main GUI:

cft

or equivalently:

python -m cft

Launch an individual module directly (forecast, verification, synthesis, zoning, download):

forecast-tool

or equivalently:

python -m cft.forecast

(substitute download-tool / verification-tool / synthesis-tool / zoning-tool, or cft.download / cft.verification / cft.synthesis / cft.zoning, as needed)

Usage — without GUI

Individual components can be called directly from Python, without launching any GUI:

from cft.functions.functions_forecast import computeModelNoGui

computeModelNoGui(config)

where config is a dictionary of forecast model settings, for example:

config = {
    'rootDir': '../test',
    'predictorYear': 2026,
    'predictorMonth': 'Jun',
    'fcstTargetSeas': 'Aug-Oct',
    'fcstTargetYear': 2026,
    'climEndYr': 2015,
    'climStartYr': 1994,
    'predictorExtents': {
        'minLon': -180.0,
        'maxLon': 180.0,
        'minLat': -60.0,
        'maxLat': 60.0,
    },
    'predictorFileName': '/work/code/csc/cft/test/GPH850_System9_CDS_Jun_s300s200e150e250_1981-2026.nc',
    'predictorVar': 'z',
    'predictorCode': 'z',
    'crossval': 'KF',
    'preproc': 'PCR',
    'regression': 'OLS',
    'timeAggregation': 'mean',
    'predictandFileName': '../test/PRCP_CHIRPSp25_IRIDL_Aug-Oct_s340s200e150e250_1981-2025.nc',
    'predictandVar': 'prcp',
    'predictandCategory': 'temperature',
    'predictandMissingValue': '-999',
    'zonesFile': None,
    'zonesAttribute': 'Name',
    'zonesAggregate': False,
    'regridPredictand': False,
    'overlayFile': '../gis/sadc/gadm41_ZAF_1.json',
}

(more to follow...)

Credits

• Piotr Wolski (wolski@csag.uct.ac.za)
• Sunshine Gamedze (sgamedze@sadc.int)

Original developer (2017–2022): Thembani Moitlhobogi

Development History

• 2010–2017: original version developed under funding from the SADC CSC project SARCIS-DR.
• 2023–2025: development funded under the SADC CSC ClimSA project.
• November 2022: code ported from the original personal repo (taxmanyana/cft) to the institutional repo, sadc-csc/cft.
• Current version: developed under funding from the First Rains project.

Source code: maintained at github.com/sadc-csc since v4.0.0. From v6.0.0, also available as a pip package: pypi.org/project/sadc-cft.