Editing IPCC:AR6/SRCCL/Chapter-2 (section)

==== 2.2.5.3 Changes in heavy precipitation ====

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A large number of extreme rainfall events have been documented over the past decades (Coumou and Rahmstorf 2012 <sup>[[#fn:r329|329]]</sup> ; Seneviratne et al. 2012 <sup>[[#fn:r330|330]]</sup> ; Trenberth 2012 <sup>[[#fn:r331|331]]</sup> ; Westra et al. 2013 <sup>[[#fn:r332|332]]</sup> ; Espinoza et al. 2014 <sup>[[#fn:r333|333]]</sup> ; Guhathakurta et al. 2017 <sup>[[#fn:r334|334]]</sup> ; Taylor et al. 2017 <sup>[[#fn:r335|335]]</sup> ; Thompson et al. 2017 <sup>[[#fn:r336|336]]</sup> ; Zilli et al. 2017 <sup>[[#fn:r337|337]]</sup> ). The observed shift in the trend distribution of precipitation extremes is more distinct than for annual mean precipitation and the global land fraction experiencing more intense precipitation events is larger than expected from internal variability (Fischer and Knutti 2014 <sup>[[#fn:r338|338]]</sup> ; Espinoza et al. 2018 <sup>[[#fn:r339|339]]</sup> ; Fischer et al. 2013 <sup>[[#fn:r340|340]]</sup> ). As a result of global warming, the number of record-breaking rainfall events globally has increased significantly by 12% during the period 1981–2010 compared to those expected due to natural multi-decadal climate variability (Lehmann et al. 2015 <sup>[[#fn:r341|341]]</sup> ). The IPCC SR15 reports robust increases in observed precipitation extremes for annual maximum 1-day precipitation (RX1day) and consecutive 5-day precipitation (RX5day) (Hoegh-Guldberg et al. 2018 <sup>[[#fn:r342|342]]</sup> ; Schleussner et al. 2017 <sup>[[#fn:r343|343]]</sup> ). A number of extreme rainfall events have been attributed to human influence (Min et al. 2011 <sup>[[#fn:r344|344]]</sup> ; Pall et al. 2011 <sup>[[#fn:r345|345]]</sup> ; Sippel and Otto 2014 <sup>[[#fn:r346|346]]</sup> ; Trenberth et al. 2015 <sup>[[#fn:r347|347]]</sup> ; Krishnan et al. 2016 <sup>[[#fn:r348|348]]</sup> ) and the largest fraction of anthropogenic influence is evident in the most rare and extreme events (Fischer and Knutti 2014 <sup>[[#fn:r349|349]]</sup> ).

A warming climate is expected to intensify the hydrological cycle as a warmer climate facilitates more water vapour in the atmosphere, as approximated by the Clausius-Clapeyron (C-C) relationship, with subsequent effects on regional extreme precipitation events (Christensen and Christensen 2003 <sup>[[#fn:r350|350]]</sup> ; Pall et al. 2007 <sup>[[#fn:r351|351]]</sup> ; Berg et al. 2013 <sup>[[#fn:r352|352]]</sup> ; Wu et al. 2013 <sup>[[#fn:r353|353]]</sup> ; Guhathakurta et al. 2017 <sup>[[#fn:r354|354]]</sup> ; Thompson et al. 2017 <sup>[[#fn:r355|355]]</sup> ; Taylor et al. 2017 <sup>[[#fn:r356|356]]</sup> ; Zilli et al. 2017 <sup>[[#fn:r357|357]]</sup> ; Manola et al. 2018 <sup>[[#fn:r358|358]]</sup> ). Furthermore, changes to the dynamics of the atmosphere amplify or weaken future precipitation extremes at the regional scale (O’Gorman 2015 <sup>[[#fn:r359|359]]</sup> ; Pfahl et al. 2017 <sup>[[#fn:r360|360]]</sup> ). Continued anthropogenic warming is very likely to increase the frequency and intensity of extreme rainfall in many regions of the globe (Seneviratne et al. 2012 <sup>[[#fn:r361|361]]</sup> ; Mohan and Rajeevan 2017 <sup>[[#fn:r362|362]]</sup> ; Prein et al. 2017 <sup>[[#fn:r363|363]]</sup> ; Stott et al. 2016 <sup>[[#fn:r364|364]]</sup> ) although many general circulation models (GCMs) underestimate observed increased trends in heavy precipitation suggesting a substantially stronger intensification of future heavy rainfall than the multi-model mean (Borodina et al. 2017 <sup>[[#fn:r365|365]]</sup> ; Min et al. 2011 <sup>[[#fn:r366|366]]</sup> ). Furthermore, the response of extreme convective precipitation to warming remains uncertain because GCMs and regional climate models (RCMs) are unable to explicitly simulate sub-grid scale processes such as convection, the hydrological cycle and surface fluxes and have to rely on parameterisation schemes for this (Crétat et al. 2012 <sup>[[#fn:r367|367]]</sup> ; Rossow et al. 2013 <sup>[[#fn:r368|368]]</sup> ; Wehner 2013 <sup>[[#fn:r369|369]]</sup> ; Kooperman et al. 2014 <sup>[[#fn:r370|370]]</sup> ; O’Gorman 2015 <sup>[[#fn:r371|371]]</sup> ; Larsen et al. 2016 <sup>[[#fn:r372|372]]</sup> ; Chawla et al. 2018 <sup>[[#fn:r373|373]]</sup> ; Kooperman et al. 2018 <sup>[[#fn:r374|374]]</sup> ; Maher et al. 2018 <sup>[[#fn:r375|375]]</sup> ; Rowell and Chadwick 2018 <sup>[[#fn:r376|376]]</sup> ). High-resolution RCMs that explicitly resolve convection have a better representation of extreme precipitation but are dependent on the GCM to capture the large scale environment in which the extreme event may occur (Ban et al. 2015 <sup>[[#fn:r377|377]]</sup> ; Prein et al. 2015 <sup>[[#fn:r378|378]]</sup> ; Kendon et al. 2017 <sup>[[#fn:r379|379]]</sup> ). Inter- annual variability of precipitation extremes in the convective tropics are not well captured by global models (Allan and Liu 2018 <sup>[[#fn:r380|380]]</sup> ).

There is ''low confidence'' in the detection of long-term observed and projected seasonal and daily trends of extreme snowfall. The narrow rain–snow transition temperature range at which extreme snowfall can occur is relatively insensitive to climate warming and subsequent large interdecadal variability (Kunkel et al. 2013 <sup>[[#fn:r381|381]]</sup> ; O’Gorman 2014 <sup>[[#fn:r382|382]]</sup> , 2015 <sup>[[#fn:r383|383]]</sup> ).

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