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<p>Droughts impact India’s water resources and agriculture, requiring enhanced seasonal forecasting systems. This study evaluates forecast skill of precipitation and 2&#xa0;m temperature using four Global Climate Models (CCSM4, GEOSS2S, CFSv2, and SEAS5) over India. Using 42 years of data, we assess 2 and 3-month lead forecasts for drought early warning systems. IMD precipitation and ERA5 2&#xa0;m temperature served as reference datasets for evaluation. Forecast performance was examined using deterministic metrics (RMSE, MAE, ACC) and categorical metrics (HSS, POD, FAR, ETS), focusing on lower tercile precipitation and upper tercile temperature events linked to drought across seasons and regions. CFSv2 provided good precipitation and drought skill during monsoon, with strong precipitation skill over Northeast and Central Northeast zones, among the highest rainfall regions. SEAS5 was the most robust model, showing superior 2&#xa0;m temperature forecast skill across seasons and exceptional drought detection in pre-monsoon, especially over Northeast India. It also demonstrated notable precipitation skills during monsoon and post-monsoon. CCSM4 offered consistent temperature forecasts, notably over Hilly regions in winter, and showed moderate precipitation skill in North West and West Central regions during monsoon. GEOSS2S exhibited stable temperature skill and effective winter drought detection, reinforcing its value in cold-season applications. Despite these strengths, models faced challenges over climatologically dry and complex regions (North West, Hilly Region, and Western Ghats). Forecast skill deteriorated with increasing lead time, particularly for precipitation, while temperature forecasts remained stable. These findings provide a systematic evaluation of model strengths and limitations, offering insights for enhancing sub-seasonal drought forecasting and early warning systems in India.</p>

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Multi-model assessment of seasonal rainfall and temperature forecast skill for operational drought prediction in India

  • 期刊论文

  • 发表日期：

    2026年1月5日

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  • Paushali Deb,
  • Saurabh Verma,
  • Lanka Karthikeyan

摘要

Droughts impact India’s water resources and agriculture, requiring enhanced seasonal forecasting systems. This study evaluates forecast skill of precipitation and 2 m temperature using four Global Climate Models (CCSM4, GEOSS2S, CFSv2, and SEAS5) over India. Using 42 years of data, we assess 2 and 3-month lead forecasts for drought early warning systems. IMD precipitation and ERA5 2 m temperature served as reference datasets for evaluation. Forecast performance was examined using deterministic metrics (RMSE, MAE, ACC) and categorical metrics (HSS, POD, FAR, ETS), focusing on lower tercile precipitation and upper tercile temperature events linked to drought across seasons and regions. CFSv2 provided good precipitation and drought skill during monsoon, with strong precipitation skill over Northeast and Central Northeast zones, among the highest rainfall regions. SEAS5 was the most robust model, showing superior 2 m temperature forecast skill across seasons and exceptional drought detection in pre-monsoon, especially over Northeast India. It also demonstrated notable precipitation skills during monsoon and post-monsoon. CCSM4 offered consistent temperature forecasts, notably over Hilly regions in winter, and showed moderate precipitation skill in North West and West Central regions during monsoon. GEOSS2S exhibited stable temperature skill and effective winter drought detection, reinforcing its value in cold-season applications. Despite these strengths, models faced challenges over climatologically dry and complex regions (North West, Hilly Region, and Western Ghats). Forecast skill deteriorated with increasing lead time, particularly for precipitation, while temperature forecasts remained stable. These findings provide a systematic evaluation of model strengths and limitations, offering insights for enhancing sub-seasonal drought forecasting and early warning systems in India.