Tchuwa et al. (2025) Projecting meteorological drought in Northern Malawi using SPEI and bias-corrected CMIP6 models

Identification

• Journal: Discover Atmosphere
• Year: 2025
• Date: 2025-10-24
• Authors: Isaac Tchuwa, Jaideep Patel
• DOI: 10.1007/s44292-025-00059-1

Research Groups

• Ndata School of Climate and Earth Sciences, Malawi University of Science and Technology, Malawi
• Institute for Water and Energy Sciences (Including Climate Change), Pan African University, Algeria

Short Summary

This study provides a high-resolution, CMIP6-based characterization of future meteorological droughts across five synoptic stations in Northern Malawi using the Standardized Precipitation Evapotranspiration Index (SPEI). Results indicate statistically significant increases in drought severity and persistence under high-emission scenarios, highlighting intensifying drought risk under warming conditions.

Objective

• To provide a high-resolution, CMIP6-based characterization of future meteorological droughts across five synoptic stations in Northern Malawi using the Standardized Precipitation Evapotranspiration Index (SPEI) at 6- and 12-month scales.

Study Configuration

• Spatial Scale: Northern Malawi (approximately 27,131 km²), focusing on five synoptic stations: Bolero, Karonga, Mzimba, Mzuzu, and Nkhata Bay.
• Temporal Scale: Historical period (1991–2020); future projections for mid-century (2021–2050) and mid–late century (2051–2080) under SSP2-4.5 and SSP5-8.5 scenarios.

Methodology and Data

• Models used:
  • Coupled Model Intercomparison Project Phase 6 (CMIP6) models: ACCESS-CM2, FIO-ESM-2-0, INM-CM5_0, MIROC6.
  • Drought Index: Standardized Precipitation Evapotranspiration Index (SPEI) at 6-month (SPEI-6) and 12-month (SPEI-12) accumulation scales.
  • Potential Evapotranspiration (PET) estimation: Modified Hargreaves method.
  • Bias correction techniques: Delta Change (DS), Quantile Mapping (QM), and Empirical Quantile Mapping (EQM).
  • Statistical downscaling.
• Data sources:
  • Observational: Long-term monthly precipitation and temperature records (1991–2020) from five synoptic stations in Northern Malawi, provided by the Department of Climate Change and Meteorological Services (DCCMS).
  • Satellite-derived: Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) for cross-validation and data supplementation.

Main Results

• Future meteorological droughts are projected to intensify significantly across Northern Malawi under both SSP2-4.5 and SSP5-8.5 scenarios, with greater severity under SSP5-8.5.
• Median SPEI-12 values at Mzimba are projected to decline from −0.42 to −0.63 between the mid-century (2021–2050) and mid–late century (2051–2080) under SSP5-8.5 (p < 0.001), indicating a strengthening of dryness. Comparable declines are projected for Mzuzu (–0.38 to −0.57, p < 0.01) and Bolero (–0.33 to −0.54, p < 0.05).
• Seasonal droughts (SPEI-6) show even steeper trends, with Mzimba’s median SPEI-6 declining by 44% and Bolero’s by 40% from mid- to late century under SSP5-8.5.
• Under SSP5-8.5, mid–long term (2051–2080) projections show SPEI-12 values at Mzimba, Bolero, and Karonga frequently plunging below −2.0, and in extreme years, below −2.5, indicating highly intensified seasonal-scale droughts.
• Model performance evaluation using Taylor diagrams showed high fidelity for temperature variables (correlation coefficients consistently exceeding 0.90). Precipitation exhibited greater model spread, but ensemble and weighted ensemble approaches mitigated individual model deficiencies. The MIROC6 model consistently performed well across all variables.
• Receiver Operating Characteristic (ROC) analysis confirmed model skill in detecting historical drought thresholds, with AUC values > 0.87 across all stations.

Contributions

• This study integrates multi-scale SPEI analysis calibrated using bias-corrected and statistically downscaled CMIP6 outputs, accounting for both precipitation variability and temperature-driven moisture stress, which is an advancement over previous precipitation-only or raw-model-based studies.
• It employs empirical quantile mapping and delta change techniques for station-level bias correction, enhancing projection fidelity in Northern Malawi's complex terrain.
• The research implements a multi-model ensemble strategy based on rigorous statistical performance evaluation, explicitly addressing epistemic and internal variability uncertainties.
• It presents the first district-level SPEI-based drought projections for Northern Malawi under alternative Shared Socioeconomic Pathways (SSP2-4.5 and SSP5-8.5), providing actionable climate risk diagnostics for sub-national policy.
• The study represents the first CMIP6-based drought assessment in Malawi that integrates station-scale temporal resolution, robust multi-scenario analysis, and rigorous model benchmarking using Taylor skill scores and ensemble diagnostics.

Funding

• A small research grant from the Pan African University Institute for Water and Energy Sciences (Including Climate Change).

Citation

@article{Tchuwa2025Projecting,
  author = {Tchuwa, Isaac and Patel, Jaideep},
  title = {Projecting meteorological drought in Northern Malawi using SPEI and bias-corrected CMIP6 models},
  journal = {Discover Atmosphere},
  year = {2025},
  doi = {10.1007/s44292-025-00059-1},
  url = {https://doi.org/10.1007/s44292-025-00059-1}
}