Understanding the spatial organization and transitions of convective cloud systems over complex terrain remains a major challenge for both observation-based analysis and numerical weather prediction (NWP). This limitation affects our ability to accurately represent and predict extreme precipitation and hail events, which are expected to intensify over the Alpine region under climate change.In this work, we present a data-driven framework based on self-supervised learning (SSL) to characterize convective cloud structures and their transitions using long-term geostationary satellite data records. By learning representations directly from infrared imagery without manual labeling, the model organizes cloud scenes based on their spatial and morphological similarities, providing a physically interpretable feature space of mesoscale cloud features. This learned representation is exploited in two complementary ways. First, it enables the analysis of convective cloud structures and their transitions associated with severe precipitation events by identifying dominant pathways linked to intense rainfall and hail over the Alpine region. The most severe precipitation is predominantly observed either in association with long-lasting deep convective systems or during transitions from early-stage convection to a more mature regime, highlighting two key modes linked to extreme precipitation.Second, the framework provides a novel tool for evaluating convection-permitting NWP simulations. By projecting synthetic satellite channels into the same feature space, it enables a direct comparison between modeled and observed cloud structures, both in terms of continuous embeddings and discrete cloud classes. Preliminary results indicate that ICON tends to overestimate the spatial extent of convective cells during their early development stages, suggesting biases in the representation of convective initiation and growth. Overall, this approach demonstrates the potential of self-supervised learning to bridge satellite observations and model simulations, providing a flexible framework for studying convective processes and assessing their representation in high-resolution weather models over complex terrain.

The World Climate Research Programme identifies critical gaps in understanding and modeling orographic precipitation. This includes characterizing pre-convective environmental conditions and understanding how they lead to precipitation onset. To address these knowledge gaps, we introduce a field campaign initiative supported by the IDEA-S4S network under the GPEX working group. As part of the TEAMx summer extensive observation period, two identical measurement sites equipped with one scanning microwave radiometer (MWR), one micro rain radar, and one disdrometer were deployed along an altitudinal transect in the Alps at approximately 1200 m and 2100 m a.s.l. on the slope of Corno del Renon, Bolzano, Italy. These two sites are embedded within a broader ground-based remote sensing network surrounding the mountain, facilitating monitoring of valley and mountain flows at larger scales. Additionally, a third MWR was installed at the KITcube supersite on the valley floor at 250 m a.s.l..Building on these measurements, we present insights from multisensor analysis of convective events observed between May and September 2025. Specifically, we investigate boundary-layer conditions for convective initiation, focusing on water vapor variability and its temporal evolution across sites, as well as precipitation variability across space and elevation. In parallel, we apply a self-supervised learning (SSL) framework to long-term geostationary satellite data to characterize convective cloud structures and their transitions, estimate growth rates of key cloud variables, and relate these findings to observed climatology. By leveraging the synergy of the campaign's multisensor observations, we advance our understanding of orographic convective processes, thereby addressing the critical gaps initially highlighted.

Convective updrafts are one of the main characteristics of convective clouds, responsible for the convective mass flux and the redistribution of energy and condensate in the atmosphere. During the early stages of their lifecycle, convective clouds experience rapid cloud-top ascent manifested by a decrease in the geostationary IR brightness temperature (TBIR). Under the assumption that the convective cloud top behaves like a black body, the ascent rate of the convective cloud top can be estimated as (∂TBIR∂t), and it can be used to infer the near cloud-top convective updraft. The temporal resolution of the geostationary IR measurements and non-uniform beam-filling effects can influence the convective updraft estimation. However, the main shortcoming until today was the lack of independent verification of the strength of the convective updraft. Here, Doppler radar observations from the ESCAPE and TRACER field experiments provide independent estimates of the convective updraft velocity at higher spatiotemporal resolution throughout the convective core column and can be used to evaluate the updraft velocity estimates from the IR cooling rate for limited samples. Isolated convective cells were tracked with dedicated radar (RHIs and PPIs) scans throughout their lifecycle. Radial Doppler velocity measurements near the convective cloud top are used to provide estimates of convective updrafts. These data are compared with the geostationary IR and VIS channels (from the GOES satellite) to characterize the convection evolution and lifecycle based on cloud-top cooling rates.

How does climate change impact extreme events and which is the future change of their dynamics? How will the ongoing and future changing climate control the evolution and intensification of severe storms? These are among the most frequent and significant questions for the scientific community, stakeholders and decision-making structures. The project tackles these open issues by investigating hailstorms in the Mediterranean region through the synergistic application of satellite observations, meteorological reanalysis and climatic modelling. Focusing on determining the atmospheric variables most relevant for the formation and intensification of hail-bearing storms, we delineate specific metrics describing the hail formation potentially applicable at operational level. The proposal stems from the 22-yearlong database of hail episodes described by Laviola et al. (2022), whereby events associated with large and extreme hail (above 2 and 10 cm in diameter, respectively) were preliminarily identified and shown to be on a 30% increase trend. Extending and refining this climatology at daily scale, the large-scale and mesoscale atmospheric scenarios that trigger hail events in the central Mediterranean area are investigated through a cluster analysis with the use of meteorological reanalysis data in the recent past. Hail-prone conditions are associated with the optimization of a hail-proxy index based on environmental variables extracted from global and regional reanalysis products. Such index and the reference hail-prone conditions are then be investigated in the ensemble of climate model projections to outline the future evolution of hail-precursors triggering and sustaining deep convection over the Mediterranean basin to the end of the century. This investigation will be also exploited to identify the environmental key variables controlling hail hazards in the recent past, and prospect future changes of storm extremization. The first-year results presented in this work delineate a new paradigm of knowledge for better understanding the effects of climate change on hailstorms by using hail-bearing convective systems as a driver for evaluating the potential impact of future changes in the Mediterranean basin. ReferenceLaviola S., G. Monte, E. Cattani, V. Levizzani, 2022: Hail Climatology in the Mediterranean Basin Using the GPM Constellation (1999-2021). Remote Sensing, 14(17), 4320. https://doi.org/10.3390/rs1417432

The Multi-sensor Approach for Satellite Hail Advection (MASHA) is a new multi-instrument technique conceived for real-time tracking of hail-bearing clouds. MASHA can identify hail clouds from satellite measurements and monitor the evolution of hail-bearing systems every 5 min, combining the strength of the MicroWave Cloud Classification-Hail (MWCC-H) method to detect hail through the whole GPM sensor constellation (Laviola et al., 2020a-b) with the high temporal rate of the Meteosat Rapid Scan Service (MSG-RSS). This opens the way to operational applications of MASHA method by offering an unprecedented support to the nowcasting of hailstorms and to regional numerical weather predictions.Recent applications experimented the ingestion in the MASHA scheme of lightning strikes and radar hail indices. This new configuration of the final products significantly refines the reconstruction of hail maps when the GPM constellation overpasses are missing. The result is a near-real time, more consistent and high-resolution hail map described by a proper Hail Severity Index (HSI). Recent applications demonstrate the ability of the MASHA technique to identify severe flash flood events in mountain catchments. These results draw new perspectives to optimally investigate hydro-meteorological events over mountain areas where more traditional methodologies might underestimate the severity of events. Thus, the MASHA scheme provides a useful tool in support to nowcasting systems of hailstorms and severe weather over complex areas.ReferencesLaviola S., V. Levizzani, R. R. Ferraro, and J. Beauchamp: Hailstorm Detection by Satellite Microwave Radiometers. Remote Sens. 2020a, 12(4), 621; https://doi.org/10.3390/rs12040621.Laviola S., G. Monte, V. Levizzani, R. R. Ferraro, and J. Beauchamp: A new method for hail detection from the GPM constellation. A prospective for a global hailstorm climatology. Remote Sens. 2020b, 12(21), 3553; https://doi.org/10.3390/rs12213553.

Estimating the frequency of extreme precipitation events, both locally and over extended areas, is key for developing risk reduction measures in present and future climates. Large areas of the world are characterized by sparse or absent rain-gauge networks, which poses significant challenges to the estimation of extreme events in many applications. Remote sensing and reanalysis datasets may contribute to filling some of these gaps, but their use meets some important obstacles: 1) remote sensing/reanalysis rainfall estimates are defined at coarse resolutions, thereby preventing direct validations against ground observations; 2) they usually span a ~20-year observation period, making it difficult to estimate the frequency of large extremes; 3) they suffer from significant uncertainties. Using the novel Metastatistical Extreme Value Distribution (MEVD) and a recent statistical downscaling technique, we compare ground and satellite-based/model estimates of rainfall to quantify the improvement achieved through downscaling in high-quantile quantification. We focus on ocean rainfall observations, which are rarely considered in validating global databases, from the Tao-Triton, Pirata, and Rama buoy networks. We quantify the estimation uncertainty for point extremes associated with the MSWEP rainfall dataset. We find that the MEVD-based extreme value downscaling approach generally improves point extreme estimates.

Millimitere (mm) and sub-millimiter (sub-mm) radiometer observation of the atmosphere from space is an appealing topic given the variety of information obtainable. The exploitation of window frequencies and various gaseous absorption bands at 50/60, 118, 183 allow for a better representation of tropospheric temperature profiles, water vapor and cloud liquid contents, as well as for hail detection, and to some extent, rainfall and snowfall estimates. These observations have shown tangible impacts on numerical weather prediction and data assimilation, climate benchmarking, hydrometeorology, extreme weather nowcasting, and civil protection. Further benefits for ice cloud retrievals are expected from observations at higher frequency, such as 243 and 664 GHz channels foreseen in the upcoming EUMETSAT Polar System-Secon Generation (EPS-SG) Ice cloud imager (ICI) sensor [1] , [2] . The increase in frequency, and consequently the reduction in wavelength, from mm to sub-mm also gives the technological advantage of reduced size of the overall system, maintaining performances unchanged, thus making it easier to implement constellation of radiometers with the glaring benefit of incrementing the repetition time of the satellite overpasses. A precursor on this topic was proposed by Prof. Marzano in 2009 [3] with the FLOwer constellation of MM-wave RADiometers (FLORAD) mission. The FLORAD concept consisted in tree small satellites (<500 kg each) in a pseudo-stationary orbit (also termed as resonant or floreal orbit) to have a repetition rate of 1 hour over the Mediterranean area with a cross-track scanner sensor named FLOMIS (FLORAD microwave imager-sounder) with channels ranging from 90 to 230 GHz. Two evolutions of FLORAD were proposed later, adding radio occultation [4] or cloud radar [5] . Ten years later, technological progress allowed the deployment of a proof-of-concept radiometer on a cubesat (1.23 kg), named TEMPEST-D [6] , as well as TROPICS [7] , a six-radiometer constellation (5.34 kg each). These missions exploit satellites that are orders of magnitude smaller and cheaper than traditional satellites operated by federal agencies, revolutionizing the next-generations of Earth-observations [8] . In Europe, the ESA/EUMETSAT prototype satellite of the Arctic Weather Satellite (AWS) mission has been recently approved. The AWS Microwave Radiometer (MWR) is a 19 channel cross-track scanning radiometer consisting of a rotating antenna focusing the incoming radiation onto four feedhorns (one for each group of channels) and four receivers, covering the frequency range 50–325 GHz. The AWS will be the forerunner of the potential EPS-Sterna mission, a constellation of small (120 kg) polar-orbiting satellites based on the AWS, each carrying a single microwave radiometer providing frequent coverage of the Earth and full coverage of the polar zones with no gaps. The EPS-Sterna would complement the MetOp series as well as the US NOAA’s Joint Polar Satellite System by providing more frequent observations mainly for temperature and humidity sounding but also for improving precipitation monitoring at high latitudes.

A new method for studying hailstorms from space offers more consistent and more complete views of how and where hail forms, and how climate change might influence hail’s impacts in the future.

The impacts of hailstorms on human beings and structures and the associated high economic costs have raised significant interest in studying storm mechanisms and climatology, thus producing a substantial amount of literature in the field. To contribute to this effort, we have explored the hail frequency in the Mediterranean basin during the last two decades (1999–2021) on the basis of hail occurrences derived from the observations of the microwave radiometers on board satellites of the Global Precipitation Measurement Constellation (GPM-C) from 2014 (date of GPM Core Observatory launch) onwards and merging multiple other satellite platforms prior to 2014. According to the MWCC-H method, two hail event categories (hail and super hail) are identified, and their spatiotemporal distributions are evaluated to identify the hail development areas in the Mediterranean and the corresponding monthly climatology of hail occurrences. Our results show that the northern sectors of the domain (France, Alpine Region, Po Valley, and Central-Eastern Europe) tend to be hit by hailstorms from June to August, while the central sectors (from Spain to Turkey) are more affected as autumn approaches. The trend analysis shows that the mean number of hail events over the entire domain tends to substantially increase, showing a higher increment during 2010–2021 than during 1999–2010. This behavior was particularly enhanced over Southern Italy and the Balkans. Our findings point to the existence of “sub-hotspots”, i.e., Mediterranean regions most susceptible to hail events and thus possibly more vulnerable to climate change effects.

Extreme precipitation heavily affects society and economy in Africa because it triggers natural hazards and contributes large amounts of freshwater. Understanding past changes in extreme precipitation could help us improve our projections of extremes, thus reducing the vulnerability of the region to climate change. Here, we combine high-resolution satellite data (1981–2019) with a novel non-asymptotic statistical approach, which explicitly separates intensity and occurrence of the process. We investigate past changes in extreme daily precipitation amounts relevant to engineering and risk management. Significant (α=0.05) positive and negative trends in annual maximum daily precipitation are reported in ∼20 % of Africa both at the local scales (0.05°) and mesoscales (1°). Our statistical model is able to explain ∼90% of their variance, and performs well (72% explained variance) even when annual maxima are explicitly censored from the parameter estimation. This suggests possible applications in situations in which the observed extremes are not quantitatively trusted. We present results at the continental scale, as well as for six areas characterized by different climatic characteristics and forcing mechanisms underlying the ongoing changes. In general, we can attribute most of the observed trends to changes in the tail heaviness of the intensity distribution (25% of explained variance, 38% at the mesoscale), while changes in the average number of wet days only explain 4% (12%) of the variance. Low-probability extremes always exhibit faster trend rates than annual maxima (∼44% faster, in median, for the case of 100-year events), implying that changes in infrastructure design values are likely underestimated by approaches based on trend analyses of annual maxima: flexible change-permitting models are needed. No systematic difference between local and mesoscales is reported, with locally-varying impacts on the areal reduction factors used to transform return levels across scales.

Quantifying past changes in extreme precipitation is crucial to understand the climate response and improve our projections. Due to the limited data availability, information about Africa is of particular interest. We combine high-resolution satellite estimates (CHIRPSv2) with an innovative approach for the detection and attribution of trends in extremes (both annual maxima and rarer events, such as the 100-year return levels) to investigate changes in daily precipitation extremes and storm structure occurred over Africa since 1981. Scale-dependence is explored by comparing trends detected at the local (0.05° resolution) and meso- (1°) scales. The statistical model was validated using a gauge-based dataset (GPCC) before application to satellite estimates. Roughly ~20% of the continent experienced significant (p=0.05) changes in annual maxima at both scales. Decreasing trends are observed in the central portion of the continent, and increasing trends in the Sahel and some districts in southern and eastern Africa. Storms tended to become spatially smoother, with faster decreases at the local scales (median=13% faster for annual maxima, 14% for 100-year return levels) and faster increases at the mesoscales (17% for annual maxima, 16% for 100-year return levels). The 100-year return levels increased 33% (25% at the mesoscale) faster than annual maxima and decreased 43% (45%) faster.The model explains 89% (91% at the mesoscale) of the variance in the observed significant trends. Changing proportions between heavy and mild events explain 25% (38%) of this variance, changes in the overall intensities 13% (21%), and changes in the number of wet days 4% (12%). About ~25% of the area experienced significant trends in at least one model parameter, although no significant trend could be detected in the maxima. Censoring annual maxima, the model still explains 77% of the variance in their trends, suggesting it could be effectively used in situations in which observed/modelled extremes are not trusted.

This paper describes the Passive microwave Neural network Precipitation Retrieval algorithm for climate applications (PNPR-CLIM), developed with funding from the Copernicus Climate Change Service (C3S), implemented by ECMWF on behalf of the European Union. The algorithm has been designed and developed to exploit the two cross-track scanning microwave radiometers, AMSU-B and MHS, towards the creation of a long-term (2000–2017) global precipitation climate data record (CDR) for the ECMWF Climate Data Store (CDS). The algorithm has been trained on an observational dataset built from one year of MHS and GPM-CO Dual-frequency Precipitation Radar (DPR) coincident observations. The dataset includes the Fundamental Climate Data Record (FCDR) of AMSU-B and MHS brightness temperatures, provided by the Fidelity and Uncertainty in Climate data records from Earth Observation (FIDUCEO) project, and the DPR-based surface precipitation rate estimates used as reference. The combined use of high quality, calibrated and harmonized long-term input data (provided by the FIDUCEO microwave brightness temperature Fundamental Climate Data Record) with the exploitation of the potential of neural networks (ability to learn and generalize) has made it possible to limit the use of ancillary model-derived environmental variables, thus reducing the model uncertainties’ influence on the PNPR-CLIM, which could compromise the accuracy of the estimates. The PNPR-CLIM estimated precipitation distribution is in good agreement with independent DPR-based estimates. A multiscale assessment of the algorithm’s performance is presented against high quality regional ground-based radar products and global precipitation datasets. The regional and global three-year (2015–2017) verification analysis shows that, despite the simplicity of the algorithm in terms of input variables and processing performance, the quality of PNPR-CLIM outperforms NASA GPROF in terms of rainfall detection, while in terms of rainfall quantification they are comparable. The global analysis evidences weaknesses at higher latitudes and in the winter at mid latitudes, mainly linked to the poorer quality of the precipitation retrieval in cold/dry conditions.

During recent decades East Africa (EA) and Southern Africa (SA) have experienced an intensification of hydrological hazards, such as floods and droughts, which have dramatically affected the population, making these areas two of the regions of the African continent most vulnerable to these hazards. Thus, precipitation monitoring and the evaluation of its variability have become fundamentally important actions through the analysis of long-term data records. In particular, satellite-based precipitation products are often used because they counterbalance the sparsity of the rain gauge networks which often characterize these areas. The aim of this work is to compare and contrast the capabilities of three daily satellite-based products in EA and SA from 1983 to 2017. The selected products are two daily rainfall datasets based on high-resolution thermal infrared observations, TAMSAT version 3 and CHIRPS, and a relatively new global product, MSWEP version 2.2, which merges satellite-based, rain gauge and re-analysis precipitation data. The datasets have been directly intercompared, avoiding the traditional rain gauge validation. This is done by means of pairwise comparison statistics at 0.25° spatial resolution and daily time scale to assess rain–detection and quantitative estimate capabilities. Monthly climatology and spatial distribution of seasonality are analyzed as well. The time evolution of the statistical indexes has been evaluated in order to analyze the stability of the rain detection and estimation performances. Considerable agreement among the precipitation products emerged from the analysis, in spite of the differences occurring in specific situations over complex terrain, such as mountainous and coastal regions and deserts. Moreover, the temporal evolution of the statistical indices has demonstrated that the agreement between the products improved over time, with more stable capabilities in identifying precipitating days and estimating daily precipitation starting in the second half of the 1990s.

Within the Copernicus Climate Change Service (C3S), the Climate Data Store (CDS) built by ECMWF will provide open and free access to global and regional products of Essential Climate Variables (ECV) based on satellite observations spanning several decades, amongst other things. Given its significance in the Earth system and particularly for human life, the ECV precipitation will be of major interest for users of the CDS. C3S strives to include as many established, high-quality data sets as possible in the CDS. However, it also intends to offer new products dedicated for first-hand publication in the CDS. One of these products is a climate data record based on merging satellite observations of daily and monthly precipitation by both passive microwave (MW) sounders (AMSU-B/MHS) and imagers (SSMI/SSMIS) on a 1°x1° spatial grid in order to improve spatiotemporal satellite coverage of the globe. The MW sounder observations will be obtained using, as input data, the FIDUCEO Fundamental Climate data Record (FCDR) for AMSU-B/MHS in a new global algorithm developed specifically for the project based on the Passive microwave Neural network Precipitation Retrieval approach (PNPR; Sanò et al., 2015), adapted for climate applications (PNPR-CLIM). The algorithm consists of two Artificial Neural Network-based modules, one for precipitation detection, and one for precipitation rate estimate, trained on a global observational database built from Global Precipitation Measurement-Core Observatory (GPM-CO) measurements. The MW imager observations by SSM/I and SSMIS will be adopted from the Hamburg Ocean Atmosphere Fluxes and Parameters from Satellite data (HOAPS; Andersson et al., 2017), based on the CM SAF SSM/I and SSMIS FCDR (Fennig et al., 2017). The Level 2 precipitation rate estimates from MW sounders and imagers are combined through a newly developed merging module to obtain Level 3 daily and monthly precipitation and generate the 18-year precipitation CDR (2000-2017). Here, we present the status of the Level 2 product’s development. We carry out a Level-2 comparison and present first results of the merged Level-3 precipitation fields. Based on this, we assess the product’s expected plausibility, coverage, and the added value of merging the MW sounder and imager observations. References Anderssonet al., 2017, DOI:10.5676/EUM_SAF_CM/HOAPS/V002 Fennig, et al., 2017, DOI:10.5676/EUM_SAF_CM/FCDR_MWI/V003 Sanò, P., et al., 2015, DOI: 10.5194/amt-8-837-2015

The water cycle is the most essential supporting physical mechanism ensuring the existence of life on Earth. Its components encompass the atmosphere, land, and oceans. The cycle is composed of evaporation, evapotranspiration, sublimation, water vapor transport, condensation, precipitation, runoff, infiltration and percolation, groundwater flow, and plant uptake. For a correct closure of the global water cycle, observations are needed of all these processes with a global perspective. In particular, precipitation requires continuous monitoring, as it is the most important component of the cycle, especially under changing climatic conditions. Passive and active sensors on board meteorological and environmental satellites now make reasonably complete data available that allow better measurements of precipitation to be made from space, in order to improve our understanding of the cycle’s acceleration/deceleration under current and projected climate conditions. The article aims to draw an up-to-date picture of the current status of observations of precipitation from space, with an outlook to the near future of the satellite constellation, modeling applications, and water resource management.

Daily time series from the Climate Prediction Center (CPC) Africa Rainfall Climatology version 2.0 (ARC2), Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) and Tropical Applications of Meteorology using SATellite (TAMSAT) African Rainfall Climatology And Time series version 2 (TARCAT) high-resolution long-term satellite rainfall products are exploited to study the spatial and temporal variability of East Africa (EA, 5S–20N, 28–52E) rainfall between 1983 and 2015. Time series of selected rainfall indices from the joint CCl/CLIVAR/JCOMM Expert Team on Climate Change Detection and Indices are computed at yearly and seasonal scales. Rainfall climatology and spatial patterns of variability are extracted via the analysis of the total rainfall amount (PRCPTOT), the simple daily intensity (SDII), the number of precipitating days (R1), the number of consecutive dry and wet days (CDD and CWD), and the number of very heavy precipitating days (R20). Our results show that the spatial patterns of such trends depend on the selected rainfall product, as much as on the geographic areas characterized by statistically significant trends for a specific rainfall index. Nevertheless, indications of rainfall trends were extracted especially at the seasonal scale. Increasing trends were identified for the October–November–December PRCPTOT, R1, and SDII indices over eastern EA, with the exception of Kenya. In March–April–May, rainfall is decreasing over a large part of EA, as demonstrated by negative trends of PRCPTOT, R1, CWD, and R20, even if a complete convergence of all satellite products is not achieved.

Rainfall from the October–November–December (OND) short rains season over East Africa (EA, 5°S–20°N, 28–52°E) were analysed during the 1983–2010 period using state‐of‐the‐art observational datasets. Links among satellite‐derived rainfall (Climate Hazards group InfraRed Precipitation with Station data, CHIRPS), sea surface temperature (Hadley Centre Sea ice and Sea Surface Temperature, HadISST1‐SST), soil moisture (Climate Change Initiative, CCI‐SM), and dynamical variables (European Centre for Medium‐Range Weather Forecasts Re‐Analysis, ERA‐Interim) are investigated to disentangle their specific role in shaping the rainfall variability over the region. In general, interannual rainfall variability is highest in the area during OND. Empirical orthogonal function (EOF) analysis is applied to the rainfall dataset to extract the dominant spatial and temporal patterns of variability. Results show that the rainfall variability is directly influenced by the SST variability in the Indian Ocean (Indian Ocean Dipole, IOD) and in the Pacific Ocean (El Niño–Southern Oscillation, ENSO), and by the local SM. The strong positive correlation (0.78) between EA short rain index and the IOD index indicates that the positive‐phase IOD (IOD+) plays a dominant role in driving OND rainfall. Moreover, IOD+ usually coincides with El Niño events and becomes stronger as the intensity of El Niño increases for years of joint events, and the Walker circulation is shifted accordingly. Furthermore, El Niño alone brings a reduction of rainfall over EA. The physical mechanism explaining the IOD+ link to EA rainfall consists of a Gill‐type response to warm western Indian Ocean SST anomalies that induces anomalous low‐level easterlies over the IO and leads to moisture convergence over EA. In general, the response of rainfall is opposite during the negative phase of the events (IOD− and La Niña).

The potential vorticity (PV) anomalies due to the intrusion of dry stratospheric air and those generated by the tropospheric diabatic latent heating are qualitatively analyzed for five Mediterranean tropical‐like cyclones (also known as Medicanes). Model simulations show the presence of an upper level PV streamer in the early stages of the cyclone, located on the left exit of a jet stream, and a middle‐low level PV anomaly generated by the convection developing around the low‐level vortex. In the mature stage, the upper level PV anomaly around the cyclone evolves differently for each case and appears somehow dependent on the lifetime. Only for the 2006 Medicane, the PV anomalies form an intense PV tower extending continuously from the lower troposphere to the lower stratosphere.