A high resolution regional reanalysis of the Indian Monsoon Data Assimilation and Analysis (IMDAA) project is made available to researchers for deeper understanding of the Indian monsoon and its variability. This 12 km resolution reanalysis covering the satellite-era from 1979 to 2018 using 4D-Var data assimilation method and the UK Met Unified Model is presently the highest resolution atmospheric reanalysis carried out for the Indian monsoon region. Conventional and satellite observations from different sources are used, including Indian surface and upper air observations, of which some were not used in any previous reanalyses. Various aspects of this reanalysis, like quality control and bias correction of observations, data assimilation system, land surface analysis, and verification of reanalysis products, are presented in this paper. Representation of important weather phenomena of each season over India in the IMDAA reanalysis verifies reasonably well against India Meteorological Department (IMD) observations and compares closely with ERA5. Salient features of the Indian summer monsoon are found to be well represented in the IMDAA reanalysis. Characteristics of major semi-permanent summer monsoon features (e.g., Low-level Jet and Tropical Easterly Jet) in IMDAA reanalysis are consistent with ERA5. The IMDAA reanalysis has captured the mean, inter-annual, and intra-seasonal variability of summer monsoon rainfall fairly well. IMDAA produces a slightly cooler winter and a hotter summer than the observations; the reverse for ERA5. IMDAA captured the fine-scale features associated with a notable heavy rainfall episode over complex terrain. In this study, the fine grid spacing nature of IMDAA is compromised due to the lack of comparable resolution observations for verification.

The Indian Monsoon Data Assimilation and Analysis (IMDAA) is a regional high‐resolution atmospheric reanalysis over the Indian subcontinent. This regional reanalysis over India is the first of its kind and is produced by the National Centre for Medium Range Weather Forecasting and Met Office, UK, in collaboration with the India Meteorological Department under the National Monsoon Mission project of the Ministry of Earth Sciences, Government of India. The reanalysis runs from 1979 to 2018, to span the era of modern meteorological satellites. This article briefly describes the IMDAA system and discusses the performance of the IMDAA during summer monsoon (June–September). This study provides evidence for substantial improvements seen in IMDAA compared to the ERA‐Interim reanalysis fields over India. The evaluation is carried out for the period of 1979–1993 for all major features associated with the Indian Monsoon to highlight improvements compared to ERA‐Interim and to document the biases. The study also demonstrates the potential use of the IMDAA data for applications such as wind resource assessment over India.

As a direct consequence of extreme monsoon rainfall throughout the summer 2022 season Pakistan experienced the worst flooding in its history. We employ a probabilistic event attribution methodology as well as a detailed assessment of the dynamics to understand the role of climate change in this event. Many of the available state-of-the-art climate models struggle to simulate these rainfall characteristics. Those that pass our evaluation test generally show a much smaller change in likelihood and intensity of extreme rainfall than the trend we found in the observations. This discrepancy suggests that long-term variability, or processes that our evaluation may not capture, can play an important role, rendering it infeasible to quantify the overall role of human-induced climate change. However, the majority of models and observations we have analysed show that intense rainfall has become heavier as Pakistan has warmed. Some of these models suggest climate change could have increased the rainfall intensity up to 50%. The devastating impacts were also driven by the proximity of human settlements, infrastructure (homes, buildings, bridges), and agricultural land to flood plains, inadequate infrastructure, limited ex-ante risk reduction capacity, an outdated river management system, underlying vulnerabilities driven by high poverty rates and socioeconomic factors (e.g. gender, age, income, and education), and ongoing political and economic instability. Both current conditions and the potential further increase in extreme peaks in rainfall over Pakistan in light of anthropogenic climate change, highlight the urgent need to reduce vulnerability to extreme weather in Pakistan.

Human activities have been implicated in the observed increase in Global Mean Surface Temperature. Over regional scales where climatic changes determine societal impacts and drive adaptation related decisions, detection and attribution (D&A) of climate change can be challenging due to the greater contribution of internal variability, greater uncertainty in regionally important forcings, greater errors in climate models, and larger observational uncertainty in many regions of the world. We examine the causes of annual and seasonal surface air temperature (TAS) changes over sub-regions (based on a demarcation of homogeneous temperature zones) of India using two observational datasets together with results from a multimodel archive of forced and unforced simulations. Our D&A analysis examines sensitivity of the results to a variety of optimal fingerprint methods and temporal-averaging choices. We can robustly attribute TAS changes over India between 1956–2005 to anthropogenic forcing mostly by greenhouse gases and partially offset by other anthropogenic forcings including aerosols and land use land cover change.

National Centre for Medium Range Weather Forecasting high-resolution regional convective-scale Unified Model with latest tropical science settings is used to evaluate vertical structure of cloud and precipitation over two prominent monsoon regions: Western Ghats (WG) and Monsoon Core Zone (MCZ). Model radar reflectivity generated using Cloud Feedback Model Intercomparison Project Observation Simulator Package along with CloudSat profiling radar reflectivity is sampled for an active synoptic situation based on a new method using Budyko's index of turbulence (BT). Regime classification based on BT-precipitation relationship is more predominant during the active monsoon period when convective-scale model's resolution increases from 4 km to 1.5 km. Model predicted precipitation and vertical distribution of hydrometeors are found to be generally in agreement with Global Precipitation Measurement products and BT-based CloudSat observation, respectively. Frequency of occurrence of radar reflectivity from model implies that the low-level clouds below freezing level is underestimated compared to the observations over both regions. In addition, high-level clouds in the model predictions are much lesser over WG than MCZ.

A method is presented for deriving probabilistic medium‐range (1‐to‐2‐week) weather pattern forecasts for India. This method uses an existing set of 30 objectively derived daily weather patterns, which provide climatological representations for unique states in the large‐scale circulation over India. Weather pattern forecast probabilities are based on the number of ensemble members objectively assigned to each weather pattern. Two summer monsoon case studies illustrate the best use of the forecasting tool within medium‐range guidance, such as highlighting the most likely weather pattern transitions and relating these to the likelihood of weather impacts. Forecast skill is evident out to at least 10–15 days. Winter dry period weather patterns have the highest forecast skill, closely followed by retreating monsoon weather patterns. In contrast, monsoon onset and break monsoon weather patterns have the lowest forecast skill. Finally, a prototype weather pattern forecast climatology application is presented for use in highlighting when extreme rainfall is more likely than normal. This application is based on weather pattern empirical probabilities of threshold exceedances using a high‐resolution regional reanalysis. The transitional pre‐ and post‐monsoon seasons have the greatest variability in rainfall across all possible weather patterns, with a slight dip in variability during the main summer monsoon season. In contrast, very little variability across weather patterns is evident during the relatively dry winter months. This highlights the times of year when a climatology‐based weather pattern forecasting approach may have its greatest benefits over that of a basic daily climatology.

The southern Indian state of Kerala experienced exceptionally high rainfall during August 2018, which led to devastating floods in many parts of the state. Prediction and early warning of severe weather events in vulnerable areas is crucial for disaster management agencies in order to protect life and property. In recent years, state-of-the-art numerical weather prediction (NWP) models have been used operationally to predict rainfall over different spatial and temporal scales. In the present paper, predictions based on the National Centre for Medium Range Weather Forecasting (NCMRWF) models (NCUM, NCUM-R and NEPS) are assessed over Kerala to demonstrate the capabilities of highresolution models. It is found that the deterministic NWP model (NCUM and NCUM-R) forecasts are accurate at shorter lead times (up to Day 3) mainly in terms of timing and, to some extent, intensity. At higher lead times (beyond Day 3), the ensemble-based probabilistic forecasts are useful and actionable. K E Y W O R D S ensemble model, extreme rainfall, probabilistic forecasts

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