A physics informed deep learning framework for rainfall forecasting in diverse climatic regions

Accurate local rainfall prediction is vital for climate-vulnerable regions such as Sindh, Pakistan, where agriculture, water management, and flood preparedness depend on reliable forecasts under highly variable hydroclimatic regimes. This study proposes a compact physics-informed neural network that embeds an explicit relative-humidity constraint into the loss function to suppress unphysical precipitation under dry conditions, operationalized by penalizing predicted rainfall when humidity falls below 60%. The architecture is a lightweight feedforward network with 5,121 parameters, trained on multi-decadal daily observations from three contrasting climatic zones of Badin (coastal), Dadu (semi-arid), and Rohri…