Archive/Assessing the Skill of CMIP6 Annual-to-Decadal Climate Forecasts at the Catchment Scale in Northeast Brazil
Assessing the Skill of CMIP6 Annual-to-Decadal Climate Forecasts at the Catchment Scale in Northeast Brazil
Gabriela Pinheiro Feitosa, Eduardo Sávio Passos Rodrigues Martins, Francisco das Chagas Vasconcelos Júnior et al.
July 7, 2026
en

Abstract

Developing effective adaptation and mitigation strategies depends on climate predictions capable of representing future conditions across multiple temporal scales. Decadal climate predictions bridge seasonal forecasting and long-term climate projections, providing near-term climate information for decision-making and adaptation planning at multi-year timescales. This study assesses the predictive skill of CMIP6 decadal precipitation forecasts from the Decadal Climate Prediction Project for three strategic catchments in state of Ceará, in the Brazilian semi-arid region. Forecast skill was assessed using deterministic and probabilistic metrics for three averaging horizons corresponding to years 1, 1–5, and 1–10 after initialization. Systematic biases were assessed and corrected. The results indicate that predictive skill varies across forecast systems, averaging horizons, and catchments. While skill was generally lower for the 1–5-year averaging horizon, several forecast systems showed positive skill relative to climatology for the 1-year and the 1–10-year averaging horizons, especially for below-normal and above-normal precipitation categories. Although bias correction reduced effectively systematic errors, it did not consistently improve forecast skill. These findings suggest potentially useful predictive skill at decadal timescales and highlight the potential of decadal climate information to provide complementary information for near-term water resources planning and drought preparedness in the Brazilian semi-arid region.

Keywords

assessingskillcmip6annual-to-decadalclimateforecastscatchmentscalenortheastbrazildevelopingeffectiveadaptationmitigationstrategiesdependspredictionscapablerepresentingfutureconditionsacrossmultipletemporal
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