Assessing Spatial Accuracy of Lightning Forecasts Over India: Supporting Impact‐Based Forecasting for Vulnerable Regions

ABSTRACT Lightning is one of the most hazardous natural phenomena, causing significant damage to life and property. In India, lightning activity peaks during pre‐monsoon and monsoon seasons. In 2022, 36% of the deaths from natural disasters were attributed to lightning. Accurate forecasting is critical for preparedness and mitigation, but complex convection processes often lead to spatial mismatches in forecasts. Spatial verification methods offer valuable insights into the accuracy of modeling systems. This study evaluates the performance of a high‐resolution (4 km) regional model, operational at the National Centre for Medium Range Weather Forecasting (NCMRWF), that is, NCUMR (NCMRWF Regional Model), in predicting lightning strikes over India during pre‐monsoon and monsoon seasons from 2021 to 2024. The Method for Object‐Based Diagnostic Evaluation (MODE) was applied to assess the model's ability to predict lightning‐prone regions. The primary objectives of this study are (i) to analyze the performance of the NCUMR model in predicting regions affected by lightning and (ii) to determine whether MODE can be used as an effective tool for forecasting lightning‐prone areas. Results demonstrate that the NCUMR model is capable of forecasting the spatial structure and distribution of lightning events with reasonable accuracy up to 3 days in advance. On Day 1, more than 88% of lightning objects for thresholds above 5 strikes/day show boundary overlap with observations, with centroid distances for 50% of matched objects remaining below 55 km. For Day 2 lead time, 83%–85% of objects show boundary overlap. On Day 3, although displacement errors increase slightly, over 85% of objects still exhibit zero boundary distance at lower thresholds, and centroid distances remain within 1°–1.5°. For all lead times, 75% of the forecasted objects have area ratios exceeding 0.7, and complexity ratios consistently above 0.7, indicating good structural agreement. While intensity is generally under‐forecasted, 90 th percentile intensity ratios exceed 0.5 in most cases. The model performs better for lower thresholds and shows improved object correspondence during the monsoon season compared to pre‐monsoon. These results confirm the utility of object‐based verification using MODE in capturing spatial aspects of lightning forecasts and highlight its potential application for real‐time impact‐based forecasting and early warning systems.