Relative humidity prediction across the Indian subcontinent using Kumaraswamy distribution based non-Gaussian model

Relative humidity (RH) significantly influences various aspects of human life, including agriculture, weather prediction, indoor air quality, and energy consumption. Its intricate non-linear behavior poses a significant challenge for accurate estimation. In the context of Indian climate change, precise forecasting of RH is vital for agriculture and weather prediction. This study proposes a new non-Gaussian approach, the Kumaraswamy seasonal autoregressive moving average (KSARMA) model, for RH prediction across India’s five climatic zones (hot-dry, warm-humid, moderate, cold, and composite). Analyzing monthly RH data from 1981 to 2021, model selection was based on diagnostic analysis and Akaike’s information criterion (AIC), with parameter estimation utilizing conditional maximum likelihood estimators (CMLE). The KSARMA model demonstrated superior predictive accuracy compared to other models (e.g., multilayer perceptron, SARIMA, Holt-Winter, and \(\beta \) $\beta$ SARMA) across all climatic zones, except the hot-dry zone, as supported by error metrics including RMSE (0.04–0.12), MAE (0.03–0.1), SMAPE (0.04–0.2), and MASE (0.5–.08).