Precipitation in France increased to 977.36 mm in 2024 from 821.87 mm in 2023. This dataset includes a chart with historical data for France Average Precipitation.

This graph shows the average annual rainfall in France from 1981 to 2010, by city, in millimetres. We can observe, that during this period, Bordeaux accumulated a precipation averaging nearly *** metre each year.

The average rainfall chart shows the average amount of total rainfall, or amount of all liquid precipitation in millimetres (mm) such as rain, drizzle, freezing rain, and hail, observed at the location for each month of the specified year. Precipitation is measured using vertical depth of water (or water equivalent in the case of solid forms) which reaches the ground during a stated period.

This statistic displays the rainfall volume in France from 2015 to 2025, per month, in millimeters. In September 2025, the rainfall volume amounted to ******* millimeters. During the same month in 2024, the volume exceeded ********* millimeters.

Precipitation in French Polynesia decreased to 1132.42 mm in 2024 from 1324.84 mm in 2023. This dataset includes a chart with historical data for French Polynesia Average Precipitation.

France FR: Average Precipitation in Depth data was reported at 867.000 mm/Year in 2014. This stayed constant from the previous number of 867.000 mm/Year for 2012. France FR: Average Precipitation in Depth data is updated yearly, averaging 867.000 mm/Year from Dec 1962 (Median) to 2014, with 12 observations. The data reached an all-time high of 867.000 mm/Year in 2014 and a record low of 867.000 mm/Year in 2014. France FR: Average Precipitation in Depth data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s France – Table FR.World Bank: Land Use, Protected Areas and National Wealth. Average precipitation is the long-term average in depth (over space and time) of annual precipitation in the country. Precipitation is defined as any kind of water that falls from clouds as a liquid or a solid.; ; Food and Agriculture Organization, electronic files and web site.; ;

France: Precipitation, mm per year: The latest value from 2022 is 867 mm per year, unchanged from 867 mm per year in 2021. In comparison, the world average is 1176 mm per year, based on data from 176 countries. Historically, the average for France from 1961 to 2022 is 867 mm per year. The minimum value, 867 mm per year, was reached in 1961 while the maximum of 867 mm per year was recorded in 1961.

France Maximum 5-day Rainfall: 25-year Return Level data was reported at 5.353 mm in 2050. France Maximum 5-day Rainfall: 25-year Return Level data is updated yearly, averaging 5.353 mm from Dec 2050 (Median) to 2050, with 1 observations. The data reached an all-time high of 5.353 mm in 2050 and a record low of 5.353 mm in 2050. France Maximum 5-day Rainfall: 25-year Return Level data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s France – Table FR.World Bank.WDI: Environmental: Climate Risk. A 25-year return level of the 5-day cumulative precipitation is the maximum precipitation sum over any 5-day period that can be expected once in an average 25-year period.;World Bank, Climate Change Knowledge Portal (https://climateknowledgeportal.worldbank.org);;

This graph shows the average number of days with precipitation in Paris from January to December 2018. According to this statistic from LAL Sprachreisen, Paris had 11 days of rainfall in average in January.

Rainfall Rate (mm/h) HDF5 European Radar composites Surface rain-rate 2 km x 2 km grid 15 minute updates at DT+15 minutes

Quality: Odyssey will generate and archive composite products from raw single site radar data using common pre-processing and compositing algorithms. Expected performance : - The target availability of composite products produced, delivered and archived will be an average of 99.0%. - Composite products produced within 15 minutes of data time on at least 95% of occasions. - Composite products delivered(1) within 20 minutes of data time on at least 95% of occasions. - Normally, there would be no downtime during maintenance slots and planned switching of nodes - Where available, gaps in the archive hosted in Météo France will be populated with data archived in Exeter within 7 working days of notification. Performance Measure: - Performance of composite availability and timeliness will be measured at the Odyssey system. - Production timeliness will be recorded as the completion time of composite generation at Odyssey. - Availability and timeliness will be measured monthly. Fault resolution: - For system or hardware faults that affect availability, the target will be to respond and fix the fault within 2 hours of notification on 98% of occasions. Contingency: - The Odyssey service will be maintained despite IT infrastructure failures at one of the Odyssey nodes. - Contingency will be provided by the back-up Odyssey node. Switching of operational status between Odyssey nodes will occur within 30 minutes of outage. - The main Odyssey archive will be hosted at Météo France and a backup hosted at the Met Office Support Cover: - The ability to switch operational Odyssey node will be provided 24 hours a day (24/7/365) - Other support activities will take place during Normal Working Hours (of the responsible member) Service Failures: A tolerable level of service failure would be: - one ‘break of up to 15 minutes in any 7 day period - one ‘break’ of up to 60 minutes in any quarter of a year - one ‘break’ over 60 minutes in any one year, with service being restored within 4 hours. A ‘break’ denotes a reduction in service delivery, however the service will be deemed to be met if the agreed alternative output is being supplied. Service description: - Instantaneous Surface Rain rate - Domain – Whole of Europe - Projection – Lambert Equal Area - Update frequency – 15 minutes - Issue time – Approximately 15 minutes after data time - Delivery method – FTP to NM(H)S via GTS/RMDCN or Internet - Available in real-time and via archive - Format: HDF5 (structure is compliant with the Eumetnet ODIM specification) Permitted use: To be confirmed Fault reporting: Reported to the UK Met Office Weather Desk. (1) Applies only to NMS receiving composite products by direct RMDCN connections

This dataset is about: Monthly mean precipitation at meteorological station la Tour de France, Têt basin.

France FR: Droughts, Floods, Extreme Temperatures: Average 1990-2009: % of Population data was reported at 0.006 % in 2009. France FR: Droughts, Floods, Extreme Temperatures: Average 1990-2009: % of Population data is updated yearly, averaging 0.006 % from Dec 2009 (Median) to 2009, with 1 observations. France FR: Droughts, Floods, Extreme Temperatures: Average 1990-2009: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s France – Table FR.World Bank.WDI: Land Use, Protected Areas and National Wealth. Droughts, floods and extreme temperatures is the annual average percentage of the population that is affected by natural disasters classified as either droughts, floods, or extreme temperature events. A drought is an extended period of time characterized by a deficiency in a region's water supply that is the result of constantly below average precipitation. A drought can lead to losses to agriculture, affect inland navigation and hydropower plants, and cause a lack of drinking water and famine. A flood is a significant rise of water level in a stream, lake, reservoir or coastal region. Extreme temperature events are either cold waves or heat waves. A cold wave can be both a prolonged period of excessively cold weather and the sudden invasion of very cold air over a large area. Along with frost it can cause damage to agriculture, infrastructure, and property. A heat wave is a prolonged period of excessively hot and sometimes also humid weather relative to normal climate patterns of a certain region. Population affected is the number of people injured, left homeless or requiring immediate assistance during a period of emergency resulting from a natural disaster; it can also include displaced or evacuated people. Average percentage of population affected is calculated by dividing the sum of total affected for the period stated by the sum of the annual population figures for the period stated.; ; EM-DAT: The OFDA/CRED International Disaster Database: www.emdat.be, Université Catholique de Louvain, Brussels (Belgium), World Bank.; ;

Rainfall accumulation (mm) HDF5 European Radar composites Hourly accumulations 2 km x 2 km grid 15 minute updates at DT+15 minutes

Quality: Odyssey will generate and archive composite products from raw single site radar data using common pre-processing and compositing algorithms. Expected performance : - The target availability of composite products produced, delivered and archived will be an average of 99.0%. - Composite products produced within 15 minutes of data time on at least 95% of occasions. - Composite products delivered(1) within 20 minutes of data time on at least 95% of occasions. - Normally, there would be no downtime during maintenance slots and planned switching of nodes - Where available, gaps in the archive hosted in Météo France will be populated with data archived in Exeter within 7 working days of notification. Performance Measure: - Performance of composite availability and timeliness will be measured at the Odyssey system. - Production timeliness will be recorded as the completion time of composite generation at Odyssey. - Availability and timeliness will be measured monthly. Fault resolution: - For system or hardware faults that affect availability, the target will be to respond and fix the fault within 2 hours of notification on 98% of occasions. Contingency: - The Odyssey service will be maintained despite IT infrastructure failures at one of the Odyssey nodes. - Contingency will be provided by the back-up Odyssey node. Switching of operational status between Odyssey nodes will occur within 30 minutes of outage. - The main Odyssey archive will be hosted at Météo France and a backup hosted at the Met Office Support Cover: - The ability to switch operational Odyssey node will be provided 24 hours a day (24/7/365) - Other support activities will take place during Normal Working Hours (of the responsible member) Service Failures: A tolerable level of service failure would be: - one ‘break of up to 15 minutes in any 7 day period - one ‘break’ of up to 60 minutes in any quarter of a year - one ‘break’ over 60 minutes in any one year, with service being restored within 4 hours. A ‘break’ denotes a reduction in service delivery, however the service will be deemed to be met if the agreed alternative output is being supplied. Service description: - Instantaneous Surface Rain rate - Domain – Whole of Europe - Projection – Lambert Equal Area - Update frequency – 15 minutes - Issue time – Approximately 15 minutes after data time - Delivery method – FTP to NM(H)S via GTS/RMDCN or Internet - Available in real-time and via archive - Format: HDF5 (structure is compliant with the Eumetnet ODIM specification) Permitted use: To be confirmed Fault reporting: Reported to the UK Met Office Weather Desk. (1) Applies only to NMS receiving composite products by direct RMDCN connections

Based on current monthly figures, on average, German climate has gotten a bit warmer. The average temperature for January 2025 was recorded at around 2 degrees Celsius, compared to 1.5 degrees a year before. In the broader context of climate change, average monthly temperatures are indicative of where the national climate is headed and whether attempts to control global warming are successful. Summer and winter Average summer temperature in Germany fluctuated in recent years, generally between 18 to 19 degrees Celsius. The season remains generally warm, and while there may not be as many hot and sunny days as in other parts of Europe, heat waves have occurred. In fact, 2023 saw 11.5 days with a temperature of at least 30 degrees, though this was a decrease compared to the year before. Meanwhile, average winter temperatures also fluctuated, but were higher in recent years, rising over four degrees on average in 2024. Figures remained in the above zero range since 2011. Numbers therefore suggest that German winters are becoming warmer, even if individual regions experiencing colder sub-zero snaps or even more snowfall may disagree. Rain, rain, go away Average monthly precipitation varied depending on the season, though sometimes figures from different times of the year were comparable. In 2024, the average monthly precipitation was highest in May and September, although rainfalls might increase in October and November with the beginning of the cold season. In the past, torrential rains have led to catastrophic flooding in Germany, with one of the most devastating being the flood of July 2021. Germany is not immune to the weather changing between two extremes, e.g. very warm spring months mostly without rain, when rain might be wished for, and then increased precipitation in other months where dry weather might be better, for example during planting and harvest seasons. Climate change remains on the agenda in all its far-reaching ways.

Hackathon Overview The GenHack 3 is a data challenge organized by École Polytechnique in 2024. The task was to construct generative models for predicting maize crop yield distributions conditional on temperatures and rainfall across multiple locations simultaneously. This hackathon was divided into two rounds, each with different levels of weather conditioning, to accurately reproduce the effects of weather on final crop yield. The detailed task description can be provided upon request. Dataset Creation The dataset was generated using a Stochastic Weather Generator (SWG) and a crop model. The SWG was trained on data from four French weather stations. This weather station data was downloaded through the INRAE CLIMATIK platform, managed by the AgroClim laboratory of Avignon, France (site available in French). The stations and their identification numbers are as follows: Montreuil-Bellay (49215002), Mons-en-Chaussée (80557001), Saint-Martin-de-Hinx (40272002), and Saint-Gènes-Champanelle (63345002). We trained a daily multi-site and multivariate SWG using the following weather variables: daily minimum and maximum temperatures, precipitation, solar irradiance, and Penman evapotranspiration. The SWG is an extension of the model described in the paper "Interpretable Seasonal Hidden Markov Model for Spatio-temporal Stochastic Rain Generation". The full training details are available in the tutorial of the Julia package StochasticWeatherGenerators.jl. The SWG generated N years of weather data, which was input into the STICS crop model for maize (see the STICS website) to produce N annual crop yield values. The parameters used in the STICS model are also described in the tutorial. The most important modification to the default parameters is that no irrigation was provided, to highlight the hydric stress on the plant. Weather Data Aggregation Daily maximum temperatures and average rainfall were aggregated into nine periods spanning April 27 to October 27 (the maize growth period): Period 1: April 27 - May 16 Period 2: May 17 - June 5 Period 3: June 6 - June 25 Period 4: June 26 - July 15 Period 5: July 16 - August 4 Period 6: August 5 - August 24 Period 7: August 25 - September 13 Period 8: September 14 - October 3 Period 9: October 4 - October 27 Details on reproducing these aggregated variables are explained in the tutorial section "Sensitivity of maize on rainfall during key growth periods". Participants were provided with these aggregated weather variables and the resulting yield data. The objective was to build a generative model capable of generating yield values conditionally on specific weather conditions (e.g., high or low rainfall). Dataset Structure The dataset provided to participants included 104 realizations, with a separate validation dataset of 105 realizations used for evaluation. Column 1 (YEAR): Year number Columns 2-10 (W_1-W_9): Mean daily temperature (°C) over each of the nine periods Columns 11-19 (W_10-W_18): Mean daily rainfall (mm/mm²) over each of the nine periods Column 20 (YIELD): Annual maize yield (t/ha)

Ce jeu de données contient des données traitées de la station météorologique installée sur le toit du bâtiment GreEn-ER à Grenoble (France) depuis 2016 : moyenne horaire de la température de l'air moyenne horaire de l'humidité relative moyenne horaire de la pression atmosphérique moyenne horaire des puissances de rayonnement solaire (global, diffus et direct) moyenne et maximum horaires de la vitesse du vent, moyenne et ecart type horaire de la direction du vent cumul horaire des précipitations type de precipitation pour chaque minute (OMM SYNOP 4680) Cette station est gérée par l'école Grenoble INP - Ense3, UGA et le laboratoire G2Elab. Elle fait partie de la plateforme formation / recherche Obs-Eau. This dataset contains processed data from the weather station located on the roof of GreEn-ER building, Grenoble, France since 2016 : average hourly air temperature, average hourly relative humidity, average hourly atmospheric pressure, average hourly solar irradiance power (global, diffuse, direct), average and maximum hourly wind horizontal speed, average and standard deviation hourly horizontal wind direction hourly depth of precipitation, every minute precipitation type (WMO SYNOP 4680). This station is managed by Grenoble INP - Ense3, UGA school and G2Elab laboratory. It is part of Obs-Eau training / research platform.

Pollinators are declining globally, with climate change implicated as an important driver. Climate change can induce phenological shifts and reduce floral resources for pollinators, but little is known about its effects on floral attractiveness and how this might cascade to affect pollinators, pollination functions and plant fitness. We used an in situ long-term drought experiment to investigate multiple impacts of reduced precipitation in a natural Mediterranean shrubland, a habitat where climate change is predicted to increase the frequency and intensity of droughts. Focusing on three insect-pollinated plant species that provide abundant rewards and support a diversity of pollinators (Cistus albidus, Salvia rosmarinus and Thymus vulgaris), we investigated the effects of drought on a suite of floral traits including nectar production and floral scent. We also measured the impact of reduced rainfall on pollinator visits, fruit set and germination in S. rosmarinus and C. albidus. Drought altered floral emissions of all three plant species qualitatively, and reduced nectar production in T. vulgaris only. Apis mellifera and Bombus gr. terrestris visited more flowers in control plots than drought plots, while small wild bees visited more flowers in drought plots than control plots. Pollinator species richness did not differ significantly between treatments. Fruit set and seed set in S. rosmarinus and C. albidus did not differ significantly between control and drought plots, but seeds from drought plots had slower germination for S. rosmarinus and marginally lower germination success in C. albidus. Synthesis. Overall, we found limited but consistent impacts of a moderate experimental drought on floral phenotype, plant reproduction and pollinator visits. Increased aridity under climate change is predicted to be stronger than the level assessed in the present study. Drought impacts will likely be stronger and this could profoundly affect the structure and functioning of plant-pollinator networks in Mediterranean ecosystems. Methods 1. Study site: CLIMED long-term drought experiment All field data were collected in February-June 2018. We used a subset of established plots that were part of the CLIMED (CLImate change effects on MEDiterranean biodiversity) long-term drought experiment situated at Massif de l’Étoile in Marseille, France (43° 22’ N, 5° 25’ E). This site has a typical woody shrub community dominated by three species: Quercus coccifera Linnaeus, 1753 (Fagaceae; anemophilous and a resource of very limited use to pollinators in the region; Ropars et al., 2020a), Salvia rosmarinus Spenn., 1835 (Lamiaceae; previously Rosmarinus officinalis; Drew et al., 2017), and Cistus albidus Linnaeus, 1753 (Cistaceae; Montès et al., 2008). Local cumulative precipitation between January and May 2018 (the flowering period surveyed) reached 291 mm, while the average precipitation between January and May for the period 2008-2018 was 205 mm (Marseille-Marignane meteorological station; www.infoclimat.fr). The site is equipped with 46 metallic control and 46 4 × 4 m rain-exclusion shelters established in October 2011, spaced by 1 to 30 m (Santonja et al., 2017). Plot locations were chosen randomly at the time of establishment of the long-term experiment, and were assigned at random to control or drought treatment (Montès et al., 2008). Gutters from rain-exclusion shelters in drought plots were designed to exclude up to 30 % and excluded on average (± SE) 12 ± 2% of precipitation between 2011 and 2018 at the centre of the plots; the intercepted water was carried away from the site with a pipe system. In control plots, the upside-down gutters intercepted a very small fraction of precipitation and rainfall reached the ground (Montès et al., 2008; Santonja et al., 2017). This water deficit attempts to mimic the mean predicted changes during the dry season in the Mediterranean area by the end of this century except in winter when rainfall is expected to increase (Giorgi & Lionello, 2008: averages for 2071-2100 relative to 1961-1990: December to February +0 to +10 %, March to May -10 to -20 %, June to August -20 to -30 %, September to November -0 to -10 %; Mariotti et al., 2015: averages for 2071-2098 relative to 1980-2005: December to February -0.1 to +0.2 mm/day, June to August -0.1 to -0.3 mm/day). The moderate but chronic experimental water deficit induced by the CLIMED experiment can alter plant physiology: carbon assimilation was reduced in C. albidus, and transpiration was reduced in C. albidus and S. rosmarinus but water use efficiency was not significantly changed in 2014 (Rodriguez-Ramirez, 2017). Between January and May 2018, permanent soil moisture probes (TDR100, Campbell Scientific Inc., Logan, Utah) measured soil moisture at 10, 20 and 40 cm in two control and two drought plots. For clarity we use the term drought to refer to the drought treatment in our study. We selected 10 control plots and 10 drought plots out of the 92 plots, based on: (i) where Thymus vulgaris Linnaeus, 1753 (Lamiaceae) was present (four plots for each treatment only) because it is an important resource for pollinators (Ropars et al., 2020a); and (ii) a high and similar percentage cover of C. albidus and S. rosmarinus. The chosen control and drought plots were homogeneously distributed throughout the site. We measured the percentage cover of each species in selected plots twice (February and June 2018). The percentage cover of S. rosmarinus, C. albidus and Q. coccifera and T. vulgaris was 21, 19, 15 and 0.5 % on average respectively in the 20 plots selected, and the community composition did not differ significantly between treatments throughout the long-term experiment. Despite such low diversity, this plant community is natural, and is representative of the site and of the type of dense, closed vegetation plant communities found in the region in areas where wildfires are ancient (> 10 years; Pimont et al., 2018). Thymus vulgaris, C. albidus and S. rosmarinus are all perennial, entomogamous shrub species; T. vulgaris is gynodioecious and obligate entomogamous (dichogamous; Arnan et al., 2014), while S. rosmarinus and C. albidus are self-compatible but with limited self-pollination (Hammer & Junghanns, 2020; Blasco & Mateu, 1995). A fourth shrub species, Ulex parviflorus Pourr., 1788, was also present but very rare (0.3 % percentage cover) with very few flowers during the study period, and other flowering species were even rarer. We did not observe any insect visit to these very rare species and hence excluded them from our study. 2. Floral traits involved in pollinator attraction 2.1. Floral scent sampling and GC-MS analysis We randomly selected up to 14 plant individuals per species in each treatment (control vs. drought) with a maximum of two (four for T. vulgaris) plants in the same plot. A few samples were lost during laboratory analysis, hence final sample sizes were 23 (control: 11; drought: 12) for S. rosmarinus, 22 (control: 11; drought: 11) for C. albidus, and 19 (control: 6 female, 6 hermaphroditic; drought: 5 female, 2 hermaphroditic) for T. vulgaris. Branches of the selected flowering plants bearing around 30-50, 2-3 or 100-400 flowers [1st-3rd quantiles] for S. rosmarinus, C. albidus and T. vulgaris respectively, were enclosed in a Nalophan bag (NA CAL, 30 cm × 30 cm, thickness 25 µm, volume ~ 2L; ETS Charles Frères, Saint-Étienne, France) connected to a pumping system maintaining a 1000 mL/min and a 200mL/min inlet and outlet air flows, respectively, provided by pumps (DC 12V, NMP850KNDC, KNF Neuberger SAS, France) powered by batteries (RS Pro 5Ah, 12V, RS Components SAS, France) and controlled by debit-metres (F65-SV1 Porter, Bronkhorst, France). Inlet air was first purified with activated charcoal (untreated, Mesh 4-8, Sigma Aldrich, USA) to limit the amount of volatiles from ambient air. Second, excess of humidity was removed using drierite (W.A. Hammond DrieriteTM Indicating Absorbents Mesh size 8, USA). Finally, ozone was filtered out through a fiberglass filter disk impregnated with sodium thiosulfate (Na2S2O3) following Pollmann et al. (2005) to limit oxidation of plant volatile organic compounds (VOCs). Air flow was first stabilized for 15 min (the time required to entirely renew the air inside the 2L-bags). VOCs were then adsorbed on a cartridge placed at the bag outlet for 10 min for S. rosmarinus and T. vulgaris, and 15 min for C. albidus. This protocol optimizes the signal-to-threshold ratio without exceeding the breakthrough volume of each VOC in the conditions of our experiment, which would distort the estimated relative composition of chemical profiles (Ormeño et al., 2007). The cartridges were made of glass tubes (Gerstel OD 6 mm for TDS2/3, RIC SAS, Lyon, France) filled with 0.120 g Carbotrap® adsorbent (matrix Carbotrap® B, 20-40 mesh, Sigma-Aldrich, France) then 0.050 g Tenax® Porous Polymer Adsorbent (matrix Tenax® GR, 20-35 mesh, Sigma-Aldrich) separated by glass wool and maintained in the tube by a fixing screen (Gerstel for TDS 2 ID 4.0 mm, RIC SAS, France) at the entrance side and glass wool at the exit side. To discriminate VOCs emitted by plants from possible environmental contamination, ambient air was sampled after every five plant samples using the same protocol. VOCs from four leaf-only plant samples per plant species were also measured to investigate which VOCs contribute most to floral scent versus leaf scent, enclosing branches of comparable size than the inflorescences of floral samples in collection bags. Sample cartridges were stored in a cooler immediately after collection, and transferred to a freezer at -20 °C as soon as possible. Prior to sampling, all cartridges had been cleaned in a Thermal Adsorbent Regenerator (RTA EcoLogicSense RG1301002, TERA Environment SARL, France) at 300 °C for 4 h. To reduce environmental variation from flowering phenology in scent

This study aimed to disentangle the relative roles of climatic, landscape and local factors affecting fruit infestation rates of winter and spring host plants by the invasive fruit fly Drosophila suzukii. We assessed infestation in Aucuba japonica, Elaeagnus ×submacrophylla (syn. Elaeagnus ×ebbingei), Mahonia aquifolium, M. japonica and Viscum album fruit in the north of France, between January and July 2022. Methods Fruit infestation rates of four fleshy-fruited plant species by the invasive fly Drosophila suzukii were measured near Amiens city in northern France between January and July 2022. Each sample was accompanied by measurements of climatic and local and landscape variables in order to identify the environmental drivers of host fruit infestations. Materials and Methods Study area The study was conducted between January and July 2022 in the region of Amiens (49°53′40″ N, 2°18′07″ E) in northern France. The region’s climate is oceanic with a mean annual temperature of 10.7°C and an average annual rainfall of 691.9 mm (data from meteorological station Dury-les-Amiens, StatIC network). The landscape of this region is generally characterised by agricultural production and consists of a mosaic of open fields cultivated for cereals, rapeseed and sugar beet, interspersed with orchards, grasslands, woodland patches and rivers. Sampling design Aucuba japonica var. variegata, Elaeagnus ×submacrophylla, Mahonia aquifolium, Mahonia japonica agg. (including a complex of related cultivars and hybrids) and V. album fruit were sampled every month, with individual plants of each species randomly sampled within a landscape area of 35 × 45 km. The number of individuals sampled per species and per month varied depending on fruit availability. When possible, a minimum of 100 fruit were randomly sampled from each plant individual, with some variation depending on seasonal availability of fruit. Traits of host plant species and fruit Collected fruit were separated into three subsets to monitor Drosophila emergence: undamaged (‘healthy’) and damaged fruit collected on the plant (‘damaged’) and on the ground (‘ground’; if present). The fruit were categorized as ‘damaged’ when they were opened / injured (incised skin) and/or rotten (brown spots). Several traits of the sampled plants were measured to characterise the local resources available for the flies and the local microhabitat. For each sampled plant, five berries were randomly selected to measure length and width and calculate volume (4/3 × π × mean radius3) and fruit skin area (4 × π × mean radius2). Five leaves were also taken, their length and width measured and the leaf surface index (length × width; Ulmer et al. 2022) calculated. Individual morphology of each plant species was characterised by measuring the minimum and maximum plant canopy diameter, the circumference of the largest trunk (except for V. album) and the total number of fruit present on the plant. Mistletoe being a parasitic shrub, we also recorded the host tree species and measured the height of the mistletoe individual on the tree (from the ground), the tree height, the crown diameter, the trunk circumference, and the number of mistletoe individuals present on the host tree and in a 20 m radius around it. Environmental variables Local, landscape and climatic variables were measured at each sampling site or extracted from online databases to examine the influence of regional and local environmental conditions on infestation rates. Local environmental conditions were described as follows. First, within a 5 m radius plot centred on the sampled plant, the cover and height of the tree, shrub and herbaceous layers were estimated, as well as soil litter thickness . Second, within a 20 m radius, the percentage of local habitats surrounding the host was recorded (e.g., orchard, woodland, grassland, swamp, crop, garden, shrub, building, hedgerow, river, pond, poplar plantation, park, road), as well as the percentage of other plant species with maturing fruit. The landscape composition around each sampled plant was then characterised. A geographic database was created using a Geographic Information System (GIS; ArcGIS Pro v.2.5, ESRI). The sampled plants were positioned in the GIS and buffers of 50, 100, 250, 500, 750, 1000, 1250, 1500, 1750, 2000, 2500 and 3000 m radii around each host tree were created for subsequent analyses of landscape composition. Landscape elements (crop, water, woodland, shrubland, grassland, road, urban area, orchard, industrial zone) were extracted from the OSO 2022 database (THEIA 2023) and updated using aerial photographs and, in buffers <100 m, field observations. Macroclimatic conditions were characterised for each sampling site using regional measurements. Daily meteorological data were retrieved from the three meteorological stations closest to each site, from 1 January 2022 to each sampling date (https://www.historique-meteo.net/france/). Daily minimum, mean and maximum temperatures, rainfall and snowfall were calculated for all sites using inverse-distance weighting (IDW) interpolation (Willmott et al. 1985) from the data from the three nearest weather stations. Accumulated degree-days (“Growing Degree Days”, GDD) were calculated using a lower threshold of 0°C between 1 January 2022 and the sampling date (Baskerville and Emin 1969). The baseline value of 0°C is a standard threshold commonly used to calculate GDD in insect and plant studies (White et al. 2012; McNeil et al. 2020). It is particularly suitable to study the temporal synchrony between insects and plant resources (Iler et al. 2013; Ulmer et al. 2022). It was also chosen because (i) active D. suzukii can be observed even at very low positive temperature (< 5°C) during winter, including during periods of snowfall (Ulmer et al. 2024), (ii) flies are able to recover from chill coma after exposure to –1°C (Wallingford et al. 2016) and (iii) mistletoe fruit can undergo freeze-thaw cycles before ripening ends (Thomas et al. 2023). From daily precipitation values, we also calculated mean daily and cumulative precipitation between 1 January 2022 and each sampling date, and within the 7- or 14-days periods preceding each sampling date. Microclimate temperatures were recorded at each sampling site using Hobo loggers (TIDBIT data logger V2 TEMP TBI-001, ONSET Company, Bourne MA, USA), recording every 60 min. In each sampling site a logger was suspended 1.5 m above the ground in the plant canopy, under the shade of a branch to avoid direct exposure to solar radiation and oriented northward. The minimum, mean and maximum air temperatures were extracted every day to compute the mean daily minimum, mean and maximum temperatures. As described for macroclimate data, GDD were calculated using Hobo logger data to characterise the local microclimate under the canopy of the sampled plants. Emergence of Drosophila species After collection, the fruit sampled from each plant were placed on wet cotton wool in cylindrical plastic transparent containers (diameter = 118 mm, height = 135 mm, volume = 1,476 cm3), covered with a nylon mesh, and maintained in a temperature-controlled room at 20°C under a 16:8 L:D regime. Adult flies emerging from the fruit were placed in 70% ethanol. They were identified to species level using Bächli et al. (2004) and specific criteria published for D. suzukii (Withers and Allemand 2012). Individuals of each species were sexed and counted using a Leica M205C stereomicroscope equipped with a Leica MC170 HD camera and the Leica Application Suite software. Infestation variables We examined the relationships between environmental variables and two common infestation variables that were either centred on the fruit (Fruit Infestation Rate for a given Drosophila species: FIR = 100 × number of emerged individuals from fruit collected from a given plant individual / total number of fruit collected from the same plant individual) or on the plant species (Plant Infestation Rate: PIR = 100 × number of infested plant individuals of a species in a month / total number of plant individuals of this same species sampled in a month). These variables can be interpreted as follows: FIR reflects the plant auto-contamination by the flies while PIR reflects fly dispersal between host plant individuals (e.g., when the FIR and PIR are both high, both auto-contamination within the plant individual and dispersal of the flies between plant individuals take place; when the FIR is high and the PIR is low, there is mostly plant auto-contamination; when the FIR is low and the PIR high, there is mostly fly dispersal; when both FIR and PIR are low, there is an absence of both auto-contamination and dispersal). These infestation variables were calculated for each fruit category (healthy or damaged fruit on the plant and fallen fruit on the ground) following Deconninck et al. (2024).

This dataset is about: Rhone river, Arles, total yearly precipitation, 1994-2000.