Logo Lanfrica
  • Home
  • Atlas
  • Insights
  • Docs
  • Sign in

© 2026 Lanfrica. All rights reserved. All copyrights of the resources shown on the Lanfrica website belong to the original copyright holders, unless explicitly stated otherwise.

Manuscript on machine learning approaches to predict seasonal precipitation.

Domain:

climate

Record type:

paper
Creator:
Heinrich-Mertshcing, ClaudioMichael, Scheuerer
Publisher:
CONFER
Host:avatar

Seasonal climate forecasts are commonly based on model runs from fully coupled forecasting

systems that use Earth system models to represent interactions between the atmosphere, ocean,

land, and other Earth-system components. Recently, machine learning methods are increasingly

being investigated for this task where large-scale climate variability is linked to local or regional

temperature or precipitation in a linear or non-linear fashion. This paper investigates the use of

machine learning methods to predict seasonal precipitation in the Greater Horn of Africa (GHA)

region through a flexible machine learning forecasting system. Indices of large-scale climate

variability–including the rate of change in individual indices as well as interactions between different

indices–are used as potential features to provide probabilistic predictions of precipitation fields.

Tercile forecasts from the interpretable machine learning algorithm are compared against the

ECMWF seasonal forecasting system (SEAS5) for three wet seasons, March to May (MAM), June to

September (JJAS) and October to December (OND), over the period 1993-2020. The machine learning

model yields significant positive skill compared to climatology in the OND season, a minimal positive

skill in the JJAS season and a skill equal to climatology in the MAM season. It is outperformed by the

ECMWF forecast in the OND season, while the ECMWF forecast is overconfident in the JJAS and

MAM seasons and receives negative skill compared to climatology. Several potential extensions of

the machine learning framework are discussed.

Visit

doi.org

Languages

Ndasa

Licenses

info:eu-repo/semantics/openAccess

Similar

Applications of machine learning to predict seasonal precipitation for East AfricaAssessment of machine learning-based approaches to improve sub-seasonal to seasonal forecasting of precipitation in Senegal.Data-driven approaches outperform dynamical models for subseasonal-to-seasonal precipitation forecasting in SenegalAchieving the third 95 in sub-Saharan Africa: application of machine learning approaches to predict viral failureMachine Learning Techniques to Predict Fetal Nutritional StatusMachine learning approaches to predict maternal anemia and Identifying associated factors among pregnant women in Sub-Saharan Africa

Applications of machine learning to predict seasonal precipitation for East Africa

Seasonal climate forecasts are commonly based on model runs from fully coupled forecasting systems t

Assessment of machine learning-based approaches to improve sub-seasonal to seasonal forecasting of precipitation in Senegal.

In Senegal, the West African monsoon (WAM) season is characterized by pronounced subseasona

Data-driven approaches outperform dynamical models for subseasonal-to-seasonal precipitation forecasting in Senegal

Reliable subseasonal-to-seasonal (S2S) precipitation forecasts during the West African monsoon are c

Achieving the third 95 in sub-Saharan Africa: application of machine learning approaches to predict viral failure

Objective: Viral failure in people with HIV (PWH) may be influenced by multiple sociob

Machine Learning Techniques to Predict Fetal Nutritional Status

Objectives: Malnutrition remains the leading cause of child mortality in Tanzania, with over 34% of

Machine learning approaches to predict maternal anemia and Identifying associated factors among pregnant women in Sub-Saharan Africa

Anemia remains a major global public health problem, affecting an estimated 1.93 billion people worl